DSC478 - Programming Machine Learning Applications

Assignment 2 - Lavinia Wang

Question 3. Data Analysis and Predictive Modeling on Census data

Dataset: adult_modified.csv Dataset description here

a. Preprocessing and data analysis:

Examine the data for missing values. In case of categorical attributes, remove instances with missing values. In the case of numeric attributes, impute and fill-in the missing values using the attribute mean.

In [1]:
# Import Modules
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import os

from sklearn import preprocessing
from sklearn import neighbors, tree, naive_bayes
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn import cross_validation
/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)
In [5]:
# Change working directory
os.chdir('/Users/lavinia/Google Drive/18winter-textbooks/CSC478/Assignment2')
In [6]:
# Load file via read_csv
adult_df = pd.read_csv("adult-modified.csv")
adult_df.head(5)
Out[6]:
age workclass education marital-status race sex hours-per-week income
0 39 Public 13 Single White Male 40 <=50K
1 50 Self-emp 13 Married White Male 13 <=50K
2 38 Private 9 Single White Male 40 <=50K
3 53 Private 7 Married Black Male 40 <=50K
4 28 Private 13 Married Black Female 40 <=50K

Exploratory Data Analysis
Understand the dataset and its variables

In [7]:
# Size of the dataset
adult_df.shape
Out[7]:
(10000, 8)
In [8]:
# Summarize the variables
adult_df.describe(include='all')
Out[8]:
age workclass education marital-status race sex hours-per-week income
count 10000 10000 10000.000000 10000 10000 10000 10000.000000 10000
unique 72 4 NaN 2 5 2 NaN 2
top 31 Private NaN Single White Male NaN <=50K
freq 284 6947 NaN 5017 8556 6703 NaN 7621
mean NaN NaN 10.076600 NaN NaN NaN 40.530300 NaN
std NaN NaN 2.548172 NaN NaN NaN 12.277197 NaN
min NaN NaN 1.000000 NaN NaN NaN 1.000000 NaN
25% NaN NaN 9.000000 NaN NaN NaN 40.000000 NaN
50% NaN NaN 10.000000 NaN NaN NaN 40.000000 NaN
75% NaN NaN 12.000000 NaN NaN NaN 45.000000 NaN
max NaN NaN 16.000000 NaN NaN NaN 99.000000 NaN
In [9]:
# Find missing values in the dataset
adult_df.isnull().sum()
Out[9]:
age               0
workclass         0
education         0
marital-status    0
race              0
sex               0
hours-per-week    0
income            0
dtype: int64

Look at the csv file then we find out that missing valus are marked w/ '?'. Locate the columns with missing values.

In [10]:
adult_df.isin(['?']).any()
Out[10]:
age                True
workclass          True
education         False
marital-status    False
race              False
sex               False
hours-per-week    False
income            False
dtype: bool
In [11]:
# Change missing numeric values to column mean
adult_df.age[adult_df['age'] == '?'] = int(adult_df.age[adult_df['age'] != '?'].astype(int).mean())
#adult_df.head(20)
/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  
In [12]:
# Replace the '?' with NA in the categorical columns
adult_dfNew = adult_df.replace('?', np.NaN)
# Drop the NAs
adult_dfNew = adult_dfNew.dropna()
adult_dfNew.shape
Out[12]:
(9412, 8)

Question: Examine the characteristics of the attributes, including relevant statistics for each attribute, histograms illustrating the distribtions of numeric attributes, bar graphs showing value counts for categorical attributes, etc.

In [13]:
adult_dfNew.dtypes
Out[13]:
age               object
workclass         object
education          int64
marital-status    object
race              object
sex               object
hours-per-week     int64
income            object
dtype: object
In [14]:
adult_dfNew.age = adult_dfNew.age.apply(int)
In [15]:
adult_dfNew.describe(include='all')
Out[15]:
age workclass education marital-status race sex hours-per-week income
count 9412.000000 9412 9412.000000 9412 9412 9412 9412.000000 9412
unique NaN 3 NaN 2 5 2 NaN 2
top NaN Private NaN Married White Male NaN <=50K
freq NaN 6947 NaN 4737 8062 6383 NaN 7093
mean 38.357310 NaN 10.125266 NaN NaN NaN 41.080217 NaN
std 12.962135 NaN 2.542118 NaN NaN NaN 11.884590 NaN
min 17.000000 NaN 1.000000 NaN NaN NaN 1.000000 NaN
25% 28.000000 NaN 9.000000 NaN NaN NaN 40.000000 NaN
50% 37.000000 NaN 10.000000 NaN NaN NaN 40.000000 NaN
75% 47.000000 NaN 13.000000 NaN NaN NaN 45.000000 NaN
max 90.000000 NaN 16.000000 NaN NaN NaN 99.000000 NaN
In [16]:
# Histogram showing the distribution of age
adult_dfNew['age'].hist(grid = False)
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1bb55588>
In [17]:
# Histogram showing the distribution of education
adult_dfNew['education'].hist(grid = False)
Out[17]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1bb32080>
In [18]:
# Histogram showing the distribution of hours-per-week
adult_dfNew['hours-per-week'].hist(grid = False)
Out[18]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1c4c6358>
In [19]:
fig = plt.figure(figsize=(10,10))
fig.subplots_adjust(hspace=.5)

ax1 = fig.add_subplot(221)
ax1.set_xlabel('Workclass')
ax1.set_ylabel('Count')
ax1.set_title("Workclass Distribution")
adult_dfNew['workclass'].value_counts().plot(kind = 'bar', grid = False)

ax1 = fig.add_subplot(222)
ax1.set_xlabel('Marital Status')
ax1.set_ylabel('Count')
ax1.set_title("Martial Status Distribution")
adult_dfNew['marital-status'].value_counts().plot(kind = 'bar', grid = False)


ax1 = fig.add_subplot(223)
ax1.set_xlabel('Race')
ax1.set_ylabel('Count')
ax1.set_title("Race Distribution")
adult_dfNew['race'].value_counts().plot(kind='bar', grid = False)

ax1 = fig.add_subplot(224)
ax1.set_xlabel('Sex')
ax1.set_ylabel('Count')
ax1.set_title("Sex Distribution")
adult_dfNew['sex'].value_counts().plot(kind='bar', grid = False)

fig2 = plt.figure(figsize=(10,10))
fig2.subplots_adjust(hspace=.5)

ax1 = fig2.add_subplot(221)
ax1.set_xlabel('Income')
ax1.set_ylabel('Count')
ax1.set_title("Income Distribution")
adult_dfNew['income'].value_counts().plot(kind='bar', grid = False)
Out[19]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1cc534e0>

Question: Perform the following cross-tabulations (including generating bar charts): education+race, workclass+income, work-class+race, and race+income. In the latter case (race+income) also create a table or chart showing percentages of each race category that fall in the low-income group. Discuss your observations from this analysis.

In [20]:
def generate_crosstab(first, second):
    cross_tab = pd.crosstab(adult_dfNew[first], adult_dfNew[second])
    plt.show(cross_tab.plot(kind="bar"))

generate_crosstab('education', 'race')
generate_crosstab('workclass', 'income')
generate_crosstab('workclass', 'race')
generate_crosstab('race', 'income')
In [21]:
# Create a table or chart showing percentages of each race category that fall in the low-income group.
pd.crosstab(adult_dfNew.race, adult_dfNew.income).apply(lambda x: x/x.sum(), axis = 1)
Out[21]:
income <=50K >50K
race
Amer-Indian 0.902174 0.097826
Asian 0.769759 0.230241
Black 0.866592 0.133408
Hispanic 0.920000 0.080000
White 0.737286 0.262714

So for each of the race we can see that most of the records n this data set are under low-income category. If we talk about each race, for the race Amer-Indian 90% of them are under low-income and around 10% are under high-income category, 77% of Asian are under low income category while 23% of them are under high-income category. For Black 87% are under low-income group and 13% are under high-income. For Hispanic 92% have income less than or equal to 50k so they are under low-income group while only 8% are in high-income category. For White 73% are under low-income category and 27% are under high-income category. Among all the races, in terms of race, highest percentage of people in low-income category are Hispanic and highest percentage of people under high-income category are White.

Question: Compare and contrast the characteristics of the low-income and high-income categories across the different attributes.

In [22]:
# Workclass vs income
pd.crosstab(adult_dfNew.workclass, adult_dfNew.income).apply(lambda x: x/x.sum(), axis = 1)
Out[22]:
income <=50K >50K
workclass
Private 0.783504 0.216496
Public 0.702354 0.297646
Self-emp 0.631533 0.368467
In [23]:
# Workclass vs income
pd.crosstab(adult_dfNew.workclass, adult_dfNew.income).apply(lambda x: x/x.sum(), axis = 1)
Out[23]:
income <=50K >50K
workclass
Private 0.783504 0.216496
Public 0.702354 0.297646
Self-emp 0.631533 0.368467
In [24]:
# Marital vs income
pd.crosstab(adult_dfNew['marital-status'], adult_dfNew.income).apply(lambda x: x/x.sum(), axis = 1)
Out[24]:
income <=50K >50K
marital-status
Married 0.576314 0.423686
Single 0.933262 0.066738
In [25]:
# Sex vs income
pd.crosstab(adult_dfNew.sex, adult_dfNew.income).apply(lambda x: x/x.sum(), axis = 1)
Out[25]:
income <=50K >50K
sex
Female 0.881149 0.118851
Male 0.693091 0.306909

From the analysis above, we see that members of the private class are most likely to make less than 50k, whereas those who are self employed are more likely to make over 50k. Even more notably, single people are signifcantly more likely to make less than 50k (93.33%) than married people (57.63%). Finally, 30% of males make more than 50k where only 11.89% of females make more than 50k.

b. Predictive Modeling and Model Evaluation:

Using either Pandas or Scikit-learn, create dummy variables for the categorical attributes. Then separate the target attribute ("income>50K") from the attributes used for training. [Note: you need to drop "income<=50K" which is also created as a dummy variable in earlier steps).]

In [26]:
#  Create dummy variables for the categorical attributes. Then separate the target attribute ("income>50K") from the attributes used for training.
adult_dfNew = pd.get_dummies(adult_dfNew)
adult_dfNew.head(5)
Out[26]:
age education hours-per-week workclass_Private workclass_Public workclass_Self-emp marital-status_Married marital-status_Single race_Amer-Indian race_Asian race_Black race_Hispanic race_White sex_Female sex_Male income_<=50K income_>50K
0 39 13 40 0 1 0 0 1 0 0 0 0 1 0 1 1 0
1 50 13 13 0 0 1 1 0 0 0 0 0 1 0 1 1 0
2 38 9 40 1 0 0 0 1 0 0 0 0 1 0 1 1 0
3 53 7 40 1 0 0 1 0 0 0 1 0 0 0 1 1 0
4 28 13 40 1 0 0 1 0 0 0 1 0 0 1 0 1 0
In [27]:
# Drop income <=50K
adult_dfNew = adult_dfNew.drop('income_<=50K', 1)
adult_dfNew.head(5)
Out[27]:
age education hours-per-week workclass_Private workclass_Public workclass_Self-emp marital-status_Married marital-status_Single race_Amer-Indian race_Asian race_Black race_Hispanic race_White sex_Female sex_Male income_>50K
0 39 13 40 0 1 0 0 1 0 0 0 0 1 0 1 0
1 50 13 13 0 0 1 1 0 0 0 0 0 1 0 1 0
2 38 9 40 1 0 0 0 1 0 0 0 0 1 0 1 0
3 53 7 40 1 0 0 1 0 0 0 1 0 0 0 1 0
4 28 13 40 1 0 0 1 0 0 0 1 0 0 1 0 0
In [28]:
# Seperate target attribute ("income>50K") from the attributes used for training.
y = adult_dfNew['income_>50K']
y.head()
Out[28]:
0    0
1    0
2    0
3    0
4    0
Name: income_>50K, dtype: uint8
In [29]:
# Remove target from rest of set
adult_dfNew = adult_dfNew.drop('income_>50K', 1)
adult_dfNew.head()
Out[29]:
age education hours-per-week workclass_Private workclass_Public workclass_Self-emp marital-status_Married marital-status_Single race_Amer-Indian race_Asian race_Black race_Hispanic race_White sex_Female sex_Male
0 39 13 40 0 1 0 0 1 0 0 0 0 1 0 1
1 50 13 13 0 0 1 1 0 0 0 0 0 1 0 1
2 38 9 40 1 0 0 0 1 0 0 0 0 1 0 1
3 53 7 40 1 0 0 1 0 0 0 1 0 0 0 1
4 28 13 40 1 0 0 1 0 0 0 1 0 0 1 0

Question: Use scikit-learn to build classifiers uisng Naive Bayes (Gaussian), decision tree (using "entropy" as selection criteria), and linear discriminant analysis (LDA). For each of these perform 10-fold crossvalidation (using cross-validation module in scikit-learn) and report the overall average accuracy.

In [30]:
# (Gaussian) naive Bayes classifier
nbclf = naive_bayes.GaussianNB()
nbclf = nbclf.fit(adult_dfNew, y)

cv_scores = cross_validation.cross_val_score(nbclf, adult_dfNew, y, cv = 10)
cv_scores
print("Overall Accuracy: %0.2f (+/- %0.2f)" % (cv_scores.mean(), cv_scores.std() * 2))
Overall Accuracy: 0.72 (+/- 0.02)
In [31]:
# Decision tree classifier
treeclf = tree.DecisionTreeClassifier(criterion = 'entropy', random_state = 9)
treeclf = treeclf.fit(adult_dfNew, y)

cv_scores = cross_validation.cross_val_score(treeclf, adult_dfNew, y, cv = 10)
cv_scores
print("Overall Accuracy: %0.2f (+/- %0.2f)" % (cv_scores.mean(), cv_scores.std() * 2))
Overall Accuracy: 0.76 (+/- 0.02)
In [32]:
# Linear Discriminant Analysis(LDA)
ldclf = LinearDiscriminantAnalysis()
ldclf = ldclf.fit(adult_dfNew, y)

cv_scores = cross_validation.cross_val_score(ldclf, adult_dfNew, y, cv = 10)
cv_scores
print("Overall Accuracy: %0.2f (+/- %0.2f)" % (cv_scores.mean(), cv_scores.std() * 2))
Overall Accuracy: 0.81 (+/- 0.02)
/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")
/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.
  warnings.warn("Variables are collinear.")

Question: For the decision tree model (generated on the full training data), generate a visualization of tree and submit it as a separate file (png, jpg, or pdf) or embed it in the Jupyter Notebook.

In [33]:
from sklearn.tree import export_graphviz
export_graphviz(treeclf,out_file='tree.dot', feature_names = adult_dfNew.columns)
In [35]:
import graphviz

with open("tree.dot") as f:
    dot_graph = f.read()
graphviz.Source(dot_graph)
Out[35]:
Tree 0 marital-status_Single <= 0.5 entropy = 0.806 samples = 9412 value = [7093, 2319] 1 education <= 11.5 entropy = 0.983 samples = 4737 value = [2730, 2007] 0->1 True 3064 education <= 12.5 entropy = 0.354 samples = 4675 value = [4363, 312] 0->3064 False 2 education <= 8.5 entropy = 0.887 samples = 3222 value = [2241, 981] 1->2 1949 hours-per-week <= 41.5 entropy = 0.907 samples = 1515 value = [489, 1026] 1->1949 3 hours-per-week <= 39.5 entropy = 0.491 samples = 551 value = [492, 59] 2->3 228 age <= 29.5 entropy = 0.93 samples = 2671 value = [1749, 922] 2->228 4 sex_Male <= 0.5 entropy = 0.178 samples = 112 value = [109, 3] 3->4 21 age <= 37.5 entropy = 0.551 samples = 439 value = [383, 56] 3->21 5 entropy = 0.0 samples = 37 value = [37, 0] 4->5 6 education <= 7.5 entropy = 0.242 samples = 75 value = [72, 3] 4->6 7 age <= 76.5 entropy = 0.183 samples = 72 value = [70, 2] 6->7 18 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 6->18 8 education <= 5.5 entropy = 0.111 samples = 68 value = [67, 1] 7->8 15 age <= 78.0 entropy = 0.811 samples = 4 value = [3, 1] 7->15 9 entropy = 0.0 samples = 44 value = [44, 0] 8->9 10 age <= 53.5 entropy = 0.25 samples = 24 value = [23, 1] 8->10 11 age <= 51.0 entropy = 0.439 samples = 11 value = [10, 1] 10->11 14 entropy = 0.0 samples = 13 value = [13, 0] 10->14 12 entropy = 0.0 samples = 10 value = [10, 0] 11->12 13 entropy = 0.0 samples = 1 value = [0, 1] 11->13 16 entropy = 0.0 samples = 1 value = [0, 1] 15->16 17 entropy = 0.0 samples = 3 value = [3, 0] 15->17 19 entropy = 0.0 samples = 2 value = [2, 0] 18->19 20 entropy = 0.0 samples = 1 value = [0, 1] 18->20 22 workclass_Self-emp <= 0.5 entropy = 0.348 samples = 153 value = [143, 10] 21->22 69 hours-per-week <= 46.0 entropy = 0.636 samples = 286 value = [240, 46] 21->69 23 age <= 28.5 entropy = 0.291 samples = 137 value = [130, 7] 22->23 60 age <= 29.0 entropy = 0.696 samples = 16 value = [13, 3] 22->60 24 entropy = 0.0 samples = 49 value = [49, 0] 23->24 25 hours-per-week <= 75.0 entropy = 0.401 samples = 88 value = [81, 7] 23->25 26 hours-per-week <= 45.5 entropy = 0.365 samples = 86 value = [80, 6] 25->26 57 hours-per-week <= 82.5 entropy = 1.0 samples = 2 value = [1, 1] 25->57 27 hours-per-week <= 43.5 entropy = 0.414 samples = 72 value = [66, 6] 26->27 56 entropy = 0.0 samples = 14 value = [14, 0] 26->56 28 age <= 29.5 entropy = 0.326 samples = 67 value = [63, 4] 27->28 51 age <= 33.0 entropy = 0.971 samples = 5 value = [3, 2] 27->51 29 education <= 7.0 entropy = 0.65 samples = 6 value = [5, 1] 28->29 32 education <= 6.5 entropy = 0.283 samples = 61 value = [58, 3] 28->32 30 entropy = 0.0 samples = 4 value = [4, 0] 29->30 31 entropy = 1.0 samples = 2 value = [1, 1] 29->31 33 age <= 34.5 entropy = 0.384 samples = 40 value = [37, 3] 32->33 50 entropy = 0.0 samples = 21 value = [21, 0] 32->50 34 education <= 5.5 entropy = 0.229 samples = 27 value = [26, 1] 33->34 41 education <= 4.5 entropy = 0.619 samples = 13 value = [11, 2] 33->41 35 entropy = 0.0 samples = 20 value = [20, 0] 34->35 36 age <= 31.5 entropy = 0.592 samples = 7 value = [6, 1] 34->36 37 workclass_Public <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 36->37 40 entropy = 0.0 samples = 3 value = [3, 0] 36->40 38 entropy = 0.918 samples = 3 value = [2, 1] 37->38 39 entropy = 0.0 samples = 1 value = [1, 0] 37->39 42 age <= 36.5 entropy = 0.764 samples = 9 value = [7, 2] 41->42 49 entropy = 0.0 samples = 4 value = [4, 0] 41->49 43 age <= 35.5 entropy = 0.918 samples = 6 value = [4, 2] 42->43 48 entropy = 0.0 samples = 3 value = [3, 0] 42->48 44 education <= 3.5 entropy = 0.811 samples = 4 value = [3, 1] 43->44 47 entropy = 1.0 samples = 2 value = [1, 1] 43->47 45 entropy = 0.0 samples = 1 value = [1, 0] 44->45 46 entropy = 0.918 samples = 3 value = [2, 1] 44->46 52 entropy = 0.0 samples = 2 value = [2, 0] 51->52 53 race_White <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 51->53 54 entropy = 0.0 samples = 1 value = [1, 0] 53->54 55 entropy = 0.0 samples = 2 value = [0, 2] 53->55 58 entropy = 0.0 samples = 1 value = [0, 1] 57->58 59 entropy = 0.0 samples = 1 value = [1, 0] 57->59 61 age <= 27.5 entropy = 0.918 samples = 9 value = [6, 3] 60->61 68 entropy = 0.0 samples = 7 value = [7, 0] 60->68 62 age <= 25.0 entropy = 0.811 samples = 8 value = [6, 2] 61->62 67 entropy = 0.0 samples = 1 value = [0, 1] 61->67 63 age <= 22.5 entropy = 1.0 samples = 4 value = [2, 2] 62->63 66 entropy = 0.0 samples = 4 value = [4, 0] 62->66 64 entropy = 0.0 samples = 2 value = [2, 0] 63->64 65 entropy = 0.0 samples = 2 value = [0, 2] 63->65 70 race_Black <= 0.5 entropy = 0.569 samples = 216 value = [187, 29] 69->70 173 hours-per-week <= 62.5 entropy = 0.8 samples = 70 value = [53, 17] 69->173 71 age <= 64.5 entropy = 0.515 samples = 191 value = [169, 22] 70->71 158 workclass_Private <= 0.5 entropy = 0.855 samples = 25 value = [18, 7] 70->158 72 workclass_Self-emp <= 0.5 entropy = 0.534 samples = 181 value = [159, 22] 71->72 157 entropy = 0.0 samples = 10 value = [10, 0] 71->157 73 education <= 5.5 entropy = 0.489 samples = 160 value = [143, 17] 72->73 144 age <= 44.5 entropy = 0.792 samples = 21 value = [16, 5] 72->144 74 age <= 43.5 entropy = 0.32 samples = 86 value = [81, 5] 73->74 101 age <= 63.5 entropy = 0.639 samples = 74 value = [62, 12] 73->101 75 sex_Female <= 0.5 entropy = 0.65 samples = 24 value = [20, 4] 74->75 94 hours-per-week <= 42.5 entropy = 0.119 samples = 62 value = [61, 1] 74->94 76 age <= 39.5 entropy = 0.469 samples = 20 value = [18, 2] 75->76 89 age <= 39.0 entropy = 1.0 samples = 4 value = [2, 2] 75->89 77 entropy = 0.0 samples = 7 value = [7, 0] 76->77 78 age <= 41.5 entropy = 0.619 samples = 13 value = [11, 2] 76->78 79 race_White <= 0.5 entropy = 0.811 samples = 8 value = [6, 2] 78->79 88 entropy = 0.0 samples = 5 value = [5, 0] 78->88 80 entropy = 0.0 samples = 2 value = [2, 0] 79->80 81 education <= 4.5 entropy = 0.918 samples = 6 value = [4, 2] 79->81 82 education <= 3.5 entropy = 0.971 samples = 5 value = [3, 2] 81->82 87 entropy = 0.0 samples = 1 value = [1, 0] 81->87 83 education <= 2.5 entropy = 0.811 samples = 4 value = [3, 1] 82->83 86 entropy = 0.0 samples = 1 value = [0, 1] 82->86 84 entropy = 0.918 samples = 3 value = [2, 1] 83->84 85 entropy = 0.0 samples = 1 value = [1, 0] 83->85 90 entropy = 0.0 samples = 1 value = [0, 1] 89->90 91 race_Asian <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 89->91 92 entropy = 0.0 samples = 2 value = [2, 0] 91->92 93 entropy = 0.0 samples = 1 value = [0, 1] 91->93 95 entropy = 0.0 samples = 56 value = [56, 0] 94->95 96 age <= 53.5 entropy = 0.65 samples = 6 value = [5, 1] 94->96 97 entropy = 0.0 samples = 4 value = [4, 0] 96->97 98 age <= 56.0 entropy = 1.0 samples = 2 value = [1, 1] 96->98 99 entropy = 0.0 samples = 1 value = [0, 1] 98->99 100 entropy = 0.0 samples = 1 value = [1, 0] 98->100 102 sex_Male <= 0.5 entropy = 0.586 samples = 71 value = [61, 10] 101->102 141 education <= 6.5 entropy = 0.918 samples = 3 value = [1, 2] 101->141 103 entropy = 0.0 samples = 9 value = [9, 0] 102->103 104 hours-per-week <= 41.0 entropy = 0.637 samples = 62 value = [52, 10] 102->104 105 education <= 6.5 entropy = 0.699 samples = 53 value = [43, 10] 104->105 140 entropy = 0.0 samples = 9 value = [9, 0] 104->140 106 age <= 59.5 entropy = 0.84 samples = 26 value = [19, 7] 105->106 129 age <= 41.5 entropy = 0.503 samples = 27 value = [24, 3] 105->129 107 age <= 57.5 entropy = 0.918 samples = 21 value = [14, 7] 106->107 128 entropy = 0.0 samples = 5 value = [5, 0] 106->128 108 age <= 53.0 entropy = 0.811 samples = 16 value = [12, 4] 107->108 123 workclass_Private <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 107->123 109 age <= 39.5 entropy = 0.946 samples = 11 value = [7, 4] 108->109 122 entropy = 0.0 samples = 5 value = [5, 0] 108->122 110 entropy = 0.0 samples = 1 value = [1, 0] 109->110 111 age <= 46.0 entropy = 0.971 samples = 10 value = [6, 4] 109->111 112 age <= 44.5 entropy = 0.918 samples = 6 value = [4, 2] 111->112 119 age <= 49.0 entropy = 1.0 samples = 4 value = [2, 2] 111->119 113 age <= 41.5 entropy = 0.971 samples = 5 value = [3, 2] 112->113 118 entropy = 0.0 samples = 1 value = [1, 0] 112->118 114 entropy = 1.0 samples = 2 value = [1, 1] 113->114 115 age <= 43.5 entropy = 0.918 samples = 3 value = [2, 1] 113->115 116 entropy = 0.0 samples = 1 value = [1, 0] 115->116 117 entropy = 1.0 samples = 2 value = [1, 1] 115->117 120 entropy = 1.0 samples = 2 value = [1, 1] 119->120 121 entropy = 1.0 samples = 2 value = [1, 1] 119->121 124 entropy = 0.0 samples = 1 value = [1, 0] 123->124 125 age <= 58.5 entropy = 0.811 samples = 4 value = [1, 3] 123->125 126 entropy = 0.0 samples = 1 value = [0, 1] 125->126 127 entropy = 0.918 samples = 3 value = [1, 2] 125->127 130 age <= 40.5 entropy = 0.863 samples = 7 value = [5, 2] 129->130 135 age <= 59.5 entropy = 0.286 samples = 20 value = [19, 1] 129->135 131 entropy = 0.0 samples = 4 value = [4, 0] 130->131 132 education <= 7.5 entropy = 0.918 samples = 3 value = [1, 2] 130->132 133 entropy = 1.0 samples = 2 value = [1, 1] 132->133 134 entropy = 0.0 samples = 1 value = [0, 1] 132->134 136 entropy = 0.0 samples = 18 value = [18, 0] 135->136 137 age <= 61.5 entropy = 1.0 samples = 2 value = [1, 1] 135->137 138 entropy = 0.0 samples = 1 value = [0, 1] 137->138 139 entropy = 0.0 samples = 1 value = [1, 0] 137->139 142 entropy = 0.0 samples = 2 value = [0, 2] 141->142 143 entropy = 0.0 samples = 1 value = [1, 0] 141->143 145 entropy = 0.0 samples = 8 value = [8, 0] 144->145 146 education <= 3.5 entropy = 0.961 samples = 13 value = [8, 5] 144->146 147 entropy = 0.0 samples = 2 value = [0, 2] 146->147 148 age <= 53.5 entropy = 0.845 samples = 11 value = [8, 3] 146->148 149 entropy = 0.0 samples = 4 value = [4, 0] 148->149 150 age <= 56.0 entropy = 0.985 samples = 7 value = [4, 3] 148->150 151 entropy = 0.0 samples = 1 value = [0, 1] 150->151 152 education <= 7.0 entropy = 0.918 samples = 6 value = [4, 2] 150->152 153 education <= 5.0 entropy = 0.722 samples = 5 value = [4, 1] 152->153 156 entropy = 0.0 samples = 1 value = [0, 1] 152->156 154 entropy = 1.0 samples = 2 value = [1, 1] 153->154 155 entropy = 0.0 samples = 3 value = [3, 0] 153->155 159 age <= 47.5 entropy = 0.811 samples = 4 value = [1, 3] 158->159 164 education <= 6.5 entropy = 0.702 samples = 21 value = [17, 4] 158->164 160 entropy = 0.0 samples = 2 value = [0, 2] 159->160 161 age <= 57.5 entropy = 1.0 samples = 2 value = [1, 1] 159->161 162 entropy = 0.0 samples = 1 value = [1, 0] 161->162 163 entropy = 0.0 samples = 1 value = [0, 1] 161->163 165 age <= 55.5 entropy = 0.918 samples = 12 value = [8, 4] 164->165 172 entropy = 0.0 samples = 9 value = [9, 0] 164->172 166 hours-per-week <= 43.5 entropy = 0.985 samples = 7 value = [3, 4] 165->166 171 entropy = 0.0 samples = 5 value = [5, 0] 165->171 167 sex_Female <= 0.5 entropy = 0.722 samples = 5 value = [1, 4] 166->167 170 entropy = 0.0 samples = 2 value = [2, 0] 166->170 168 entropy = 0.0 samples = 4 value = [0, 4] 167->168 169 entropy = 0.0 samples = 1 value = [1, 0] 167->169 174 sex_Female <= 0.5 entropy = 0.897 samples = 51 value = [35, 16] 173->174 223 education <= 6.5 entropy = 0.297 samples = 19 value = [18, 1] 173->223 175 workclass_Self-emp <= 0.5 entropy = 0.843 samples = 48 value = [35, 13] 174->175 222 entropy = 0.0 samples = 3 value = [0, 3] 174->222 176 education <= 6.5 entropy = 0.734 samples = 34 value = [27, 7] 175->176 205 hours-per-week <= 59.5 entropy = 0.985 samples = 14 value = [8, 6] 175->205 177 education <= 3.5 entropy = 0.871 samples = 24 value = [17, 7] 176->177 204 entropy = 0.0 samples = 10 value = [10, 0] 176->204 178 entropy = 0.0 samples = 5 value = [5, 0] 177->178 179 hours-per-week <= 57.5 entropy = 0.949 samples = 19 value = [12, 7] 177->179 180 age <= 50.5 entropy = 0.779 samples = 13 value = [10, 3] 179->180 193 race_White <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] 179->193 181 entropy = 0.0 samples = 4 value = [4, 0] 180->181 182 hours-per-week <= 47.5 entropy = 0.918 samples = 9 value = [6, 3] 180->182 183 entropy = 0.0 samples = 1 value = [0, 1] 182->183 184 age <= 52.0 entropy = 0.811 samples = 8 value = [6, 2] 182->184 185 race_White <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 184->185 188 age <= 59.5 entropy = 0.65 samples = 6 value = [5, 1] 184->188 186 entropy = 0.0 samples = 1 value = [1, 0] 185->186 187 entropy = 0.0 samples = 1 value = [0, 1] 185->187 189 entropy = 0.0 samples = 4 value = [4, 0] 188->189 190 workclass_Public <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 188->190 191 entropy = 0.0 samples = 1 value = [0, 1] 190->191 192 entropy = 0.0 samples = 1 value = [1, 0] 190->192 194 entropy = 0.0 samples = 1 value = [0, 1] 193->194 195 age <= 41.5 entropy = 0.971 samples = 5 value = [2, 3] 193->195 196 entropy = 0.0 samples = 1 value = [0, 1] 195->196 197 education <= 5.5 entropy = 1.0 samples = 4 value = [2, 2] 195->197 198 age <= 54.0 entropy = 0.918 samples = 3 value = [1, 2] 197->198 203 entropy = 0.0 samples = 1 value = [1, 0] 197->203 199 entropy = 0.0 samples = 1 value = [0, 1] 198->199 200 education <= 4.5 entropy = 1.0 samples = 2 value = [1, 1] 198->200 201 entropy = 0.0 samples = 1 value = [1, 0] 200->201 202 entropy = 0.0 samples = 1 value = [0, 1] 200->202 206 education <= 5.5 entropy = 0.994 samples = 11 value = [5, 6] 205->206 221 entropy = 0.0 samples = 3 value = [3, 0] 205->221 207 age <= 57.5 entropy = 0.991 samples = 9 value = [5, 4] 206->207 220 entropy = 0.0 samples = 2 value = [0, 2] 206->220 208 age <= 41.5 entropy = 0.985 samples = 7 value = [3, 4] 207->208 219 entropy = 0.0 samples = 2 value = [2, 0] 207->219 209 entropy = 0.0 samples = 1 value = [1, 0] 208->209 210 hours-per-week <= 52.5 entropy = 0.918 samples = 6 value = [2, 4] 208->210 211 education <= 3.5 entropy = 1.0 samples = 4 value = [2, 2] 210->211 218 entropy = 0.0 samples = 2 value = [0, 2] 210->218 212 entropy = 0.0 samples = 1 value = [1, 0] 211->212 213 age <= 54.0 entropy = 0.918 samples = 3 value = [1, 2] 211->213 214 entropy = 0.0 samples = 1 value = [0, 1] 213->214 215 age <= 55.5 entropy = 1.0 samples = 2 value = [1, 1] 213->215 216 entropy = 0.0 samples = 1 value = [1, 0] 215->216 217 entropy = 0.0 samples = 1 value = [0, 1] 215->217 224 entropy = 0.0 samples = 14 value = [14, 0] 223->224 225 age <= 49.5 entropy = 0.722 samples = 5 value = [4, 1] 223->225 226 entropy = 0.0 samples = 4 value = [4, 0] 225->226 227 entropy = 0.0 samples = 1 value = [0, 1] 225->227 229 age <= 23.5 entropy = 0.59 samples = 345 value = [296, 49] 228->229 378 hours-per-week <= 34.5 entropy = 0.955 samples = 2326 value = [1453, 873] 228->378 230 entropy = 0.0 samples = 68 value = [68, 0] 229->230 231 hours-per-week <= 64.0 entropy = 0.673 samples = 277 value = [228, 49] 229->231 232 age <= 28.5 entropy = 0.643 samples = 263 value = [220, 43] 231->232 367 age <= 25.5 entropy = 0.985 samples = 14 value = [8, 6] 231->367 233 race_Black <= 0.5 entropy = 0.588 samples = 198 value = [170, 28] 232->233 326 race_Asian <= 0.5 entropy = 0.779 samples = 65 value = [50, 15] 232->326 234 hours-per-week <= 41.0 entropy = 0.615 samples = 184 value = [156, 28] 233->234 325 entropy = 0.0 samples = 14 value = [14, 0] 233->325 235 race_Asian <= 0.5 entropy = 0.52 samples = 120 value = [106, 14] 234->235 292 hours-per-week <= 44.5 entropy = 0.758 samples = 64 value = [50, 14] 234->292 236 workclass_Public <= 0.5 entropy = 0.483 samples = 115 value = [103, 12] 235->236 287 sex_Male <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] 235->287 237 education <= 9.5 entropy = 0.51 samples = 106 value = [94, 12] 236->237 286 entropy = 0.0 samples = 9 value = [9, 0] 236->286 238 age <= 24.5 entropy = 0.404 samples = 62 value = [57, 5] 237->238 263 hours-per-week <= 35.5 entropy = 0.632 samples = 44 value = [37, 7] 237->263 239 entropy = 0.0 samples = 10 value = [10, 0] 238->239 240 hours-per-week <= 28.0 entropy = 0.457 samples = 52 value = [47, 5] 238->240 241 sex_Male <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 240->241 244 workclass_Private <= 0.5 entropy = 0.408 samples = 49 value = [45, 4] 240->244 242 entropy = 0.0 samples = 2 value = [2, 0] 241->242 243 entropy = 0.0 samples = 1 value = [0, 1] 241->243 245 age <= 26.0 entropy = 0.722 samples = 5 value = [4, 1] 244->245 250 age <= 25.5 entropy = 0.359 samples = 44 value = [41, 3] 244->250 246 hours-per-week <= 35.0 entropy = 0.918 samples = 3 value = [2, 1] 245->246 249 entropy = 0.0 samples = 2 value = [2, 0] 245->249 247 entropy = 0.0 samples = 1 value = [1, 0] 246->247 248 entropy = 1.0 samples = 2 value = [1, 1] 246->248 251 entropy = 0.0 samples = 11 value = [11, 0] 250->251 252 sex_Male <= 0.5 entropy = 0.439 samples = 33 value = [30, 3] 250->252 253 age <= 26.5 entropy = 0.722 samples = 5 value = [4, 1] 252->253 256 age <= 26.5 entropy = 0.371 samples = 28 value = [26, 2] 252->256 254 entropy = 1.0 samples = 2 value = [1, 1] 253->254 255 entropy = 0.0 samples = 3 value = [3, 0] 253->255 257 entropy = 0.0 samples = 4 value = [4, 0] 256->257 258 hours-per-week <= 37.5 entropy = 0.414 samples = 24 value = [22, 2] 256->258 259 entropy = 0.0 samples = 1 value = [1, 0] 258->259 260 age <= 27.5 entropy = 0.426 samples = 23 value = [21, 2] 258->260 261 entropy = 0.503 samples = 9 value = [8, 1] 260->261 262 entropy = 0.371 samples = 14 value = [13, 1] 260->262 264 entropy = 0.0 samples = 8 value = [8, 0] 263->264 265 hours-per-week <= 37.0 entropy = 0.711 samples = 36 value = [29, 7] 263->265 266 entropy = 0.0 samples = 1 value = [0, 1] 265->266 267 age <= 26.5 entropy = 0.661 samples = 35 value = [29, 6] 265->267 268 sex_Male <= 0.5 entropy = 0.469 samples = 20 value = [18, 2] 267->268 275 education <= 10.5 entropy = 0.837 samples = 15 value = [11, 4] 267->275 269 entropy = 0.0 samples = 3 value = [3, 0] 268->269 270 age <= 24.5 entropy = 0.523 samples = 17 value = [15, 2] 268->270 271 entropy = 0.811 samples = 4 value = [3, 1] 270->271 272 age <= 25.5 entropy = 0.391 samples = 13 value = [12, 1] 270->272 273 entropy = 0.0 samples = 4 value = [4, 0] 272->273 274 entropy = 0.503 samples = 9 value = [8, 1] 272->274 276 age <= 27.5 entropy = 0.918 samples = 12 value = [8, 4] 275->276 285 entropy = 0.0 samples = 3 value = [3, 0] 275->285 277 sex_Female <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 276->277 280 sex_Male <= 0.5 entropy = 0.544 samples = 8 value = [7, 1] 276->280 278 entropy = 0.918 samples = 3 value = [1, 2] 277->278 279 entropy = 0.0 samples = 1 value = [0, 1] 277->279 281 entropy = 0.0 samples = 2 value = [2, 0] 280->281 282 workclass_Self-emp <= 0.5 entropy = 0.65 samples = 6 value = [5, 1] 280->282 283 entropy = 0.722 samples = 5 value = [4, 1] 282->283 284 entropy = 0.0 samples = 1 value = [1, 0] 282->284 288 entropy = 0.0 samples = 2 value = [2, 0] 287->288 289 age <= 27.5 entropy = 0.918 samples = 3 value = [1, 2] 287->289 290 entropy = 0.0 samples = 2 value = [0, 2] 289->290 291 entropy = 0.0 samples = 1 value = [1, 0] 289->291 293 workclass_Private <= 0.5 entropy = 0.985 samples = 7 value = [3, 4] 292->293 300 hours-per-week <= 51.5 entropy = 0.67 samples = 57 value = [47, 10] 292->300 294 entropy = 0.0 samples = 1 value = [1, 0] 293->294 295 age <= 25.5 entropy = 0.918 samples = 6 value = [2, 4] 293->295 296 entropy = 0.0 samples = 3 value = [0, 3] 295->296 297 age <= 27.0 entropy = 0.918 samples = 3 value = [2, 1] 295->297 298 entropy = 0.0 samples = 2 value = [2, 0] 297->298 299 entropy = 0.0 samples = 1 value = [0, 1] 297->299 301 hours-per-week <= 47.0 entropy = 0.769 samples = 40 value = [31, 9] 300->301 320 age <= 24.5 entropy = 0.323 samples = 17 value = [16, 1] 300->320 302 age <= 24.5 entropy = 0.353 samples = 15 value = [14, 1] 301->302 307 sex_Female <= 0.5 entropy = 0.904 samples = 25 value = [17, 8] 301->307 303 sex_Male <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 302->303 306 entropy = 0.0 samples = 12 value = [12, 0] 302->306 304 entropy = 0.0 samples = 1 value = [1, 0] 303->304 305 entropy = 1.0 samples = 2 value = [1, 1] 303->305 308 age <= 27.5 entropy = 0.828 samples = 23 value = [17, 6] 307->308 319 entropy = 0.0 samples = 2 value = [0, 2] 307->319 309 age <= 26.5 entropy = 0.523 samples = 17 value = [15, 2] 308->309 316 hours-per-week <= 49.0 entropy = 0.918 samples = 6 value = [2, 4] 308->316 310 age <= 25.5 entropy = 0.764 samples = 9 value = [7, 2] 309->310 315 entropy = 0.0 samples = 8 value = [8, 0] 309->315 311 entropy = 0.0 samples = 5 value = [5, 0] 310->311 312 education <= 9.5 entropy = 1.0 samples = 4 value = [2, 2] 310->312 313 entropy = 0.0 samples = 1 value = [0, 1] 312->313 314 entropy = 0.918 samples = 3 value = [2, 1] 312->314 317 entropy = 0.0 samples = 2 value = [2, 0] 316->317 318 entropy = 0.0 samples = 4 value = [0, 4] 316->318 321 hours-per-week <= 58.0 entropy = 1.0 samples = 2 value = [1, 1] 320->321 324 entropy = 0.0 samples = 15 value = [15, 0] 320->324 322 entropy = 0.0 samples = 1 value = [1, 0] 321->322 323 entropy = 0.0 samples = 1 value = [0, 1] 321->323 327 workclass_Self-emp <= 0.5 entropy = 0.798 samples = 62 value = [47, 15] 326->327 366 entropy = 0.0 samples = 3 value = [3, 0] 326->366 328 hours-per-week <= 39.0 entropy = 0.757 samples = 55 value = [43, 12] 327->328 359 sex_Male <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 327->359 329 entropy = 0.0 samples = 5 value = [5, 0] 328->329 330 sex_Female <= 0.5 entropy = 0.795 samples = 50 value = [38, 12] 328->330 331 education <= 10.5 entropy = 0.755 samples = 46 value = [36, 10] 330->331 358 entropy = 1.0 samples = 4 value = [2, 2] 330->358 332 race_Amer-Indian <= 0.5 entropy = 0.79 samples = 38 value = [29, 9] 331->332 355 hours-per-week <= 44.0 entropy = 0.544 samples = 8 value = [7, 1] 331->355 333 hours-per-week <= 57.5 entropy = 0.8 samples = 37 value = [28, 9] 332->333 354 entropy = 0.0 samples = 1 value = [1, 0] 332->354 334 race_Hispanic <= 0.5 entropy = 0.811 samples = 36 value = [27, 9] 333->334 353 entropy = 0.0 samples = 1 value = [1, 0] 333->353 335 hours-per-week <= 47.5 entropy = 0.822 samples = 35 value = [26, 9] 334->335 352 entropy = 0.0 samples = 1 value = [1, 0] 334->352 336 education <= 9.5 entropy = 0.784 samples = 30 value = [23, 7] 335->336 347 education <= 9.5 entropy = 0.971 samples = 5 value = [3, 2] 335->347 337 hours-per-week <= 42.5 entropy = 0.667 samples = 23 value = [19, 4] 336->337 342 race_White <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 336->342 338 race_White <= 0.5 entropy = 0.722 samples = 20 value = [16, 4] 337->338 341 entropy = 0.0 samples = 3 value = [3, 0] 337->341 339 entropy = 0.0 samples = 2 value = [2, 0] 338->339 340 entropy = 0.764 samples = 18 value = [14, 4] 338->340 343 entropy = 0.0 samples = 1 value = [0, 1] 342->343 344 hours-per-week <= 42.5 entropy = 0.918 samples = 6 value = [4, 2] 342->344 345 entropy = 0.811 samples = 4 value = [3, 1] 344->345 346 entropy = 1.0 samples = 2 value = [1, 1] 344->346 348 hours-per-week <= 52.5 entropy = 0.918 samples = 3 value = [1, 2] 347->348 351 entropy = 0.0 samples = 2 value = [2, 0] 347->351 349 entropy = 0.0 samples = 1 value = [0, 1] 348->349 350 entropy = 1.0 samples = 2 value = [1, 1] 348->350 356 entropy = 0.722 samples = 5 value = [4, 1] 355->356 357 entropy = 0.0 samples = 3 value = [3, 0] 355->357 360 entropy = 0.0 samples = 1 value = [0, 1] 359->360 361 hours-per-week <= 57.5 entropy = 0.918 samples = 6 value = [4, 2] 359->361 362 hours-per-week <= 47.5 entropy = 0.722 samples = 5 value = [4, 1] 361->362 365 entropy = 0.0 samples = 1 value = [0, 1] 361->365 363 entropy = 0.918 samples = 3 value = [2, 1] 362->363 364 entropy = 0.0 samples = 2 value = [2, 0] 362->364 368 education <= 10.5 entropy = 0.722 samples = 5 value = [1, 4] 367->368 371 race_Black <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] 367->371 369 entropy = 0.0 samples = 4 value = [0, 4] 368->369 370 entropy = 0.0 samples = 1 value = [1, 0] 368->370 372 workclass_Private <= 0.5 entropy = 0.544 samples = 8 value = [7, 1] 371->372 377 entropy = 0.0 samples = 1 value = [0, 1] 371->377 373 age <= 27.5 entropy = 1.0 samples = 2 value = [1, 1] 372->373 376 entropy = 0.0 samples = 6 value = [6, 0] 372->376 374 entropy = 0.0 samples = 1 value = [0, 1] 373->374 375 entropy = 0.0 samples = 1 value = [1, 0] 373->375 379 education <= 9.5 entropy = 0.55 samples = 228 value = [199, 29] 378->379 476 age <= 35.5 entropy = 0.972 samples = 2098 value = [1254, 844] 378->476 380 hours-per-week <= 15.5 entropy = 0.336 samples = 145 value = [136, 9] 379->380 423 age <= 61.5 entropy = 0.797 samples = 83 value = [63, 20] 379->423 381 entropy = 0.0 samples = 26 value = [26, 0] 380->381 382 sex_Male <= 0.5 entropy = 0.387 samples = 119 value = [110, 9] 380->382 383 race_White <= 0.5 entropy = 0.537 samples = 57 value = [50, 7] 382->383 416 hours-per-week <= 24.5 entropy = 0.206 samples = 62 value = [60, 2] 382->416 384 entropy = 0.0 samples = 10 value = [10, 0] 383->384 385 hours-per-week <= 21.0 entropy = 0.607 samples = 47 value = [40, 7] 383->385 386 workclass_Self-emp <= 0.5 entropy = 0.831 samples = 19 value = [14, 5] 385->386 403 hours-per-week <= 28.5 entropy = 0.371 samples = 28 value = [26, 2] 385->403 387 age <= 47.5 entropy = 0.65 samples = 12 value = [10, 2] 386->387 394 age <= 44.0 entropy = 0.985 samples = 7 value = [4, 3] 386->394 388 age <= 44.0 entropy = 0.863 samples = 7 value = [5, 2] 387->388 393 entropy = 0.0 samples = 5 value = [5, 0] 387->393 389 hours-per-week <= 18.0 entropy = 0.65 samples = 6 value = [5, 1] 388->389 392 entropy = 0.0 samples = 1 value = [0, 1] 388->392 390 entropy = 0.0 samples = 1 value = [0, 1] 389->390 391 entropy = 0.0 samples = 5 value = [5, 0] 389->391 395 entropy = 0.0 samples = 1 value = [1, 0] 394->395 396 age <= 62.5 entropy = 1.0 samples = 6 value = [3, 3] 394->396 397 entropy = 0.0 samples = 2 value = [0, 2] 396->397 398 age <= 70.5 entropy = 0.811 samples = 4 value = [3, 1] 396->398 399 entropy = 0.0 samples = 2 value = [2, 0] 398->399 400 age <= 76.0 entropy = 1.0 samples = 2 value = [1, 1] 398->400 401 entropy = 0.0 samples = 1 value = [0, 1] 400->401 402 entropy = 0.0 samples = 1 value = [1, 0] 400->402 404 entropy = 0.0 samples = 14 value = [14, 0] 403->404 405 hours-per-week <= 31.0 entropy = 0.592 samples = 14 value = [12, 2] 403->405 406 age <= 38.5 entropy = 0.764 samples = 9 value = [7, 2] 405->406 415 entropy = 0.0 samples = 5 value = [5, 0] 405->415 407 age <= 36.5 entropy = 1.0 samples = 2 value = [1, 1] 406->407 410 age <= 54.5 entropy = 0.592 samples = 7 value = [6, 1] 406->410 408 entropy = 0.0 samples = 1 value = [1, 0] 407->408 409 entropy = 0.0 samples = 1 value = [0, 1] 407->409 411 entropy = 0.0 samples = 4 value = [4, 0] 410->411 412 age <= 61.5 entropy = 0.918 samples = 3 value = [2, 1] 410->412 413 entropy = 1.0 samples = 2 value = [1, 1] 412->413 414 entropy = 0.0 samples = 1 value = [1, 0] 412->414 417 age <= 60.5 entropy = 0.345 samples = 31 value = [29, 2] 416->417 422 entropy = 0.0 samples = 31 value = [31, 0] 416->422 418 age <= 56.5 entropy = 0.592 samples = 14 value = [12, 2] 417->418 421 entropy = 0.0 samples = 17 value = [17, 0] 417->421 419 entropy = 0.0 samples = 12 value = [12, 0] 418->419 420 entropy = 0.0 samples = 2 value = [0, 2] 418->420 424 race_White <= 0.5 entropy = 0.905 samples = 53 value = [36, 17] 423->424 461 workclass_Self-emp <= 0.5 entropy = 0.469 samples = 30 value = [27, 3] 423->461 425 entropy = 0.0 samples = 6 value = [6, 0] 424->425 426 workclass_Private <= 0.5 entropy = 0.944 samples = 47 value = [30, 17] 424->426 427 hours-per-week <= 22.0 entropy = 0.65 samples = 18 value = [15, 3] 426->427 438 age <= 32.5 entropy = 0.999 samples = 29 value = [15, 14] 426->438 428 age <= 33.5 entropy = 0.918 samples = 9 value = [6, 3] 427->428 437 entropy = 0.0 samples = 9 value = [9, 0] 427->437 429 entropy = 0.0 samples = 1 value = [0, 1] 428->429 430 age <= 57.0 entropy = 0.811 samples = 8 value = [6, 2] 428->430 431 age <= 38.0 entropy = 0.592 samples = 7 value = [6, 1] 430->431 436 entropy = 0.0 samples = 1 value = [0, 1] 430->436 432 hours-per-week <= 14.0 entropy = 1.0 samples = 2 value = [1, 1] 431->432 435 entropy = 0.0 samples = 5 value = [5, 0] 431->435 433 entropy = 0.0 samples = 1 value = [1, 0] 432->433 434 entropy = 0.0 samples = 1 value = [0, 1] 432->434 439 entropy = 0.0 samples = 4 value = [4, 0] 438->439 440 hours-per-week <= 33.0 entropy = 0.99 samples = 25 value = [11, 14] 438->440 441 hours-per-week <= 31.0 entropy = 0.98 samples = 24 value = [10, 14] 440->441 460 entropy = 0.0 samples = 1 value = [1, 0] 440->460 442 hours-per-week <= 27.5 entropy = 0.994 samples = 22 value = [10, 12] 441->442 459 entropy = 0.0 samples = 2 value = [0, 2] 441->459 443 hours-per-week <= 17.0 entropy = 0.971 samples = 20 value = [8, 12] 442->443 458 entropy = 0.0 samples = 2 value = [2, 0] 442->458 444 age <= 43.5 entropy = 0.954 samples = 8 value = [5, 3] 443->444 449 age <= 53.0 entropy = 0.811 samples = 12 value = [3, 9] 443->449 445 entropy = 0.0 samples = 4 value = [4, 0] 444->445 446 sex_Male <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 444->446 447 entropy = 0.0 samples = 3 value = [0, 3] 446->447 448 entropy = 0.0 samples = 1 value = [1, 0] 446->448 450 age <= 38.5 entropy = 0.544 samples = 8 value = [1, 7] 449->450 455 education <= 10.5 entropy = 1.0 samples = 4 value = [2, 2] 449->455 451 age <= 37.5 entropy = 1.0 samples = 2 value = [1, 1] 450->451 454 entropy = 0.0 samples = 6 value = [0, 6] 450->454 452 entropy = 0.0 samples = 1 value = [0, 1] 451->452 453 entropy = 0.0 samples = 1 value = [1, 0] 451->453 456 entropy = 0.0 samples = 2 value = [0, 2] 455->456 457 entropy = 0.0 samples = 2 value = [2, 0] 455->457 462 entropy = 0.0 samples = 19 value = [19, 0] 461->462 463 age <= 62.5 entropy = 0.845 samples = 11 value = [8, 3] 461->463 464 entropy = 0.0 samples = 2 value = [2, 0] 463->464 465 hours-per-week <= 12.5 entropy = 0.918 samples = 9 value = [6, 3] 463->465 466 age <= 63.5 entropy = 1.0 samples = 4 value = [2, 2] 465->466 473 age <= 71.0 entropy = 0.722 samples = 5 value = [4, 1] 465->473 467 entropy = 0.0 samples = 1 value = [0, 1] 466->467 468 education <= 10.5 entropy = 0.918 samples = 3 value = [2, 1] 466->468 469 hours-per-week <= 9.0 entropy = 1.0 samples = 2 value = [1, 1] 468->469 472 entropy = 0.0 samples = 1 value = [1, 0] 468->472 470 entropy = 0.0 samples = 1 value = [1, 0] 469->470 471 entropy = 0.0 samples = 1 value = [0, 1] 469->471 474 entropy = 0.0 samples = 4 value = [4, 0] 473->474 475 entropy = 0.0 samples = 1 value = [0, 1] 473->475 477 hours-per-week <= 47.0 entropy = 0.894 samples = 431 value = [297, 134] 476->477 764 age <= 62.5 entropy = 0.984 samples = 1667 value = [957, 710] 476->764 478 race_White <= 0.5 entropy = 0.808 samples = 282 value = [212, 70] 477->478 627 sex_Male <= 0.5 entropy = 0.986 samples = 149 value = [85, 64] 477->627 479 race_Amer-Indian <= 0.5 entropy = 0.384 samples = 40 value = [37, 3] 478->479 494 sex_Male <= 0.5 entropy = 0.851 samples = 242 value = [175, 67] 478->494 480 education <= 9.5 entropy = 0.303 samples = 37 value = [35, 2] 479->480 491 education <= 9.5 entropy = 0.918 samples = 3 value = [2, 1] 479->491 481 entropy = 0.0 samples = 26 value = [26, 0] 480->481 482 age <= 32.0 entropy = 0.684 samples = 11 value = [9, 2] 480->482 483 entropy = 0.0 samples = 7 value = [7, 0] 482->483 484 workclass_Public <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 482->484 485 race_Black <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 484->485 490 entropy = 0.0 samples = 1 value = [0, 1] 484->490 486 entropy = 0.0 samples = 1 value = [1, 0] 485->486 487 age <= 33.5 entropy = 1.0 samples = 2 value = [1, 1] 485->487 488 entropy = 0.0 samples = 1 value = [1, 0] 487->488 489 entropy = 0.0 samples = 1 value = [0, 1] 487->489 492 entropy = 0.0 samples = 1 value = [0, 1] 491->492 493 entropy = 0.0 samples = 2 value = [2, 0] 491->493 495 hours-per-week <= 43.0 entropy = 0.958 samples = 29 value = [18, 11] 494->495 524 education <= 10.5 entropy = 0.831 samples = 213 value = [157, 56] 494->524 496 education <= 10.5 entropy = 0.983 samples = 26 value = [15, 11] 495->496 523 entropy = 0.0 samples = 3 value = [3, 0] 495->523 497 age <= 30.5 entropy = 0.932 samples = 23 value = [15, 8] 496->497 522 entropy = 0.0 samples = 3 value = [0, 3] 496->522 498 entropy = 0.0 samples = 3 value = [3, 0] 497->498 499 workclass_Public <= 0.5 entropy = 0.971 samples = 20 value = [12, 8] 497->499 500 age <= 32.5 entropy = 0.949 samples = 19 value = [12, 7] 499->500 521 entropy = 0.0 samples = 1 value = [0, 1] 499->521 501 age <= 31.5 entropy = 0.592 samples = 7 value = [6, 1] 500->501 506 age <= 33.5 entropy = 1.0 samples = 12 value = [6, 6] 500->506 502 education <= 9.5 entropy = 0.811 samples = 4 value = [3, 1] 501->502 505 entropy = 0.0 samples = 3 value = [3, 0] 501->505 503 entropy = 0.918 samples = 3 value = [2, 1] 502->503 504 entropy = 0.0 samples = 1 value = [1, 0] 502->504 507 hours-per-week <= 37.5 entropy = 0.811 samples = 4 value = [1, 3] 506->507 512 workclass_Private <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] 506->512 508 entropy = 0.0 samples = 1 value = [0, 1] 507->508 509 hours-per-week <= 41.0 entropy = 0.918 samples = 3 value = [1, 2] 507->509 510 entropy = 1.0 samples = 2 value = [1, 1] 509->510 511 entropy = 0.0 samples = 1 value = [0, 1] 509->511 513 entropy = 0.0 samples = 1 value = [1, 0] 512->513 514 education <= 9.5 entropy = 0.985 samples = 7 value = [4, 3] 512->514 515 hours-per-week <= 39.0 entropy = 0.918 samples = 6 value = [4, 2] 514->515 520 entropy = 0.0 samples = 1 value = [0, 1] 514->520 516 entropy = 0.0 samples = 1 value = [1, 0] 515->516 517 age <= 34.5 entropy = 0.971 samples = 5 value = [3, 2] 515->517 518 entropy = 0.811 samples = 4 value = [3, 1] 517->518 519 entropy = 0.0 samples = 1 value = [0, 1] 517->519 525 hours-per-week <= 45.5 entropy = 0.849 samples = 196 value = [142, 54] 524->525 614 age <= 30.5 entropy = 0.523 samples = 17 value = [15, 2] 524->614 526 hours-per-week <= 37.0 entropy = 0.853 samples = 194 value = [140, 54] 525->526 613 entropy = 0.0 samples = 2 value = [2, 0] 525->613 527 age <= 33.5 entropy = 0.544 samples = 8 value = [7, 1] 526->527 532 age <= 33.5 entropy = 0.862 samples = 186 value = [133, 53] 526->532 528 entropy = 0.0 samples = 4 value = [4, 0] 527->528 529 age <= 34.5 entropy = 0.811 samples = 4 value = [3, 1] 527->529 530 entropy = 0.0 samples = 1 value = [0, 1] 529->530 531 entropy = 0.0 samples = 3 value = [3, 0] 529->531 533 hours-per-week <= 38.5 entropy = 0.894 samples = 119 value = [82, 37] 532->533 582 workclass_Private <= 0.5 entropy = 0.793 samples = 67 value = [51, 16] 532->582 534 entropy = 0.0 samples = 1 value = [0, 1] 533->534 535 workclass_Public <= 0.5 entropy = 0.887 samples = 118 value = [82, 36] 533->535 536 hours-per-week <= 39.5 entropy = 0.908 samples = 105 value = [71, 34] 535->536 573 age <= 32.5 entropy = 0.619 samples = 13 value = [11, 2] 535->573 537 entropy = 0.0 samples = 1 value = [1, 0] 536->537 538 hours-per-week <= 40.5 entropy = 0.912 samples = 104 value = [70, 34] 536->538 539 age <= 30.5 entropy = 0.899 samples = 92 value = [63, 29] 538->539 562 hours-per-week <= 42.0 entropy = 0.98 samples = 12 value = [7, 5] 538->562 540 education <= 9.5 entropy = 0.855 samples = 25 value = [18, 7] 539->540 545 education <= 9.5 entropy = 0.913 samples = 67 value = [45, 22] 539->545 541 entropy = 0.764 samples = 18 value = [14, 4] 540->541 542 workclass_Self-emp <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 540->542 543 entropy = 1.0 samples = 6 value = [3, 3] 542->543 544 entropy = 0.0 samples = 1 value = [1, 0] 542->544 546 workclass_Private <= 0.5 entropy = 0.925 samples = 47 value = [31, 16] 545->546 555 workclass_Private <= 0.5 entropy = 0.881 samples = 20 value = [14, 6] 545->555 547 age <= 31.5 entropy = 0.985 samples = 7 value = [4, 3] 546->547 550 age <= 32.5 entropy = 0.91 samples = 40 value = [27, 13] 546->550 548 entropy = 1.0 samples = 2 value = [1, 1] 547->548 549 entropy = 0.971 samples = 5 value = [3, 2] 547->549 551 age <= 31.5 entropy = 0.918 samples = 27 value = [18, 9] 550->551 554 entropy = 0.89 samples = 13 value = [9, 4] 550->554 552 entropy = 0.918 samples = 12 value = [8, 4] 551->552 553 entropy = 0.918 samples = 15 value = [10, 5] 551->553 556 entropy = 0.0 samples = 3 value = [3, 0] 555->556 557 age <= 31.5 entropy = 0.937 samples = 17 value = [11, 6] 555->557 558 entropy = 0.881 samples = 10 value = [7, 3] 557->558 559 age <= 32.5 entropy = 0.985 samples = 7 value = [4, 3] 557->559 560 entropy = 0.971 samples = 5 value = [3, 2] 559->560 561 entropy = 1.0 samples = 2 value = [1, 1] 559->561 563 entropy = 0.0 samples = 1 value = [0, 1] 562->563 564 education <= 9.5 entropy = 0.946 samples = 11 value = [7, 4] 562->564 565 age <= 32.0 entropy = 0.985 samples = 7 value = [3, 4] 564->565 572 entropy = 0.0 samples = 4 value = [4, 0] 564->572 566 hours-per-week <= 44.0 entropy = 0.811 samples = 4 value = [1, 3] 565->566 569 hours-per-week <= 44.0 entropy = 0.918 samples = 3 value = [2, 1] 565->569 567 entropy = 0.0 samples = 1 value = [0, 1] 566->567 568 entropy = 0.918 samples = 3 value = [1, 2] 566->568 570 entropy = 0.0 samples = 1 value = [1, 0] 569->570 571 entropy = 1.0 samples = 2 value = [1, 1] 569->571 574 age <= 30.5 entropy = 0.439 samples = 11 value = [10, 1] 573->574 579 education <= 9.5 entropy = 1.0 samples = 2 value = [1, 1] 573->579 575 education <= 9.5 entropy = 0.918 samples = 3 value = [2, 1] 574->575 578 entropy = 0.0 samples = 8 value = [8, 0] 574->578 576 entropy = 1.0 samples = 2 value = [1, 1] 575->576 577 entropy = 0.0 samples = 1 value = [1, 0] 575->577 580 entropy = 0.0 samples = 1 value = [1, 0] 579->580 581 entropy = 0.0 samples = 1 value = [0, 1] 579->581 583 hours-per-week <= 42.5 entropy = 0.954 samples = 16 value = [10, 6] 582->583 598 education <= 9.5 entropy = 0.714 samples = 51 value = [41, 10] 582->598 584 education <= 9.5 entropy = 0.985 samples = 14 value = [8, 6] 583->584 597 entropy = 0.0 samples = 2 value = [2, 0] 583->597 585 age <= 34.5 entropy = 0.991 samples = 9 value = [4, 5] 584->585 592 age <= 34.5 entropy = 0.722 samples = 5 value = [4, 1] 584->592 586 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 585->586 589 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] 585->589 587 entropy = 0.0 samples = 1 value = [1, 0] 586->587 588 entropy = 1.0 samples = 2 value = [1, 1] 586->588 590 entropy = 0.811 samples = 4 value = [1, 3] 589->590 591 entropy = 1.0 samples = 2 value = [1, 1] 589->591 593 hours-per-week <= 39.0 entropy = 0.918 samples = 3 value = [2, 1] 592->593 596 entropy = 0.0 samples = 2 value = [2, 0] 592->596 594 entropy = 0.0 samples = 1 value = [1, 0] 593->594 595 entropy = 1.0 samples = 2 value = [1, 1] 593->595 599 hours-per-week <= 43.5 entropy = 0.614 samples = 33 value = [28, 5] 598->599 608 age <= 34.5 entropy = 0.852 samples = 18 value = [13, 5] 598->608 600 age <= 34.5 entropy = 0.491 samples = 28 value = [25, 3] 599->600 605 hours-per-week <= 44.5 entropy = 0.971 samples = 5 value = [3, 2] 599->605 601 entropy = 0.0 samples = 14 value = [14, 0] 600->601 602 hours-per-week <= 41.5 entropy = 0.75 samples = 14 value = [11, 3] 600->602 603 entropy = 0.779 samples = 13 value = [10, 3] 602->603 604 entropy = 0.0 samples = 1 value = [1, 0] 602->604 606 entropy = 0.0 samples = 1 value = [0, 1] 605->606 607 entropy = 0.811 samples = 4 value = [3, 1] 605->607 609 entropy = 1.0 samples = 8 value = [4, 4] 608->609 610 hours-per-week <= 42.5 entropy = 0.469 samples = 10 value = [9, 1] 608->610 611 entropy = 0.503 samples = 9 value = [8, 1] 610->611 612 entropy = 0.0 samples = 1 value = [1, 0] 610->612 615 entropy = 0.0 samples = 4 value = [4, 0] 614->615 616 hours-per-week <= 41.0 entropy = 0.619 samples = 13 value = [11, 2] 614->616 617 age <= 34.5 entropy = 0.764 samples = 9 value = [7, 2] 616->617 626 entropy = 0.0 samples = 4 value = [4, 0] 616->626 618 workclass_Public <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 617->618 625 entropy = 0.0 samples = 2 value = [2, 0] 617->625 619 age <= 31.5 entropy = 0.918 samples = 6 value = [4, 2] 618->619 624 entropy = 0.0 samples = 1 value = [1, 0] 618->624 620 entropy = 1.0 samples = 2 value = [1, 1] 619->620 621 age <= 33.0 entropy = 0.811 samples = 4 value = [3, 1] 619->621 622 entropy = 0.0 samples = 1 value = [1, 0] 621->622 623 entropy = 0.918 samples = 3 value = [2, 1] 621->623 628 age <= 30.5 entropy = 0.544 samples = 8 value = [7, 1] 627->628 631 age <= 32.5 entropy = 0.992 samples = 141 value = [78, 63] 627->631 629 entropy = 0.0 samples = 1 value = [0, 1] 628->629 630 entropy = 0.0 samples = 7 value = [7, 0] 628->630 632 hours-per-week <= 54.5 entropy = 0.954 samples = 64 value = [40, 24] 631->632 695 hours-per-week <= 49.0 entropy = 1.0 samples = 77 value = [38, 39] 631->695 633 workclass_Private <= 0.5 entropy = 0.758 samples = 32 value = [25, 7] 632->633 658 race_Hispanic <= 0.5 entropy = 0.997 samples = 32 value = [15, 17] 632->658 634 entropy = 0.0 samples = 6 value = [6, 0] 633->634 635 hours-per-week <= 51.5 entropy = 0.84 samples = 26 value = [19, 7] 633->635 636 race_Asian <= 0.5 entropy = 0.887 samples = 23 value = [16, 7] 635->636 657 entropy = 0.0 samples = 3 value = [3, 0] 635->657 637 education <= 10.5 entropy = 0.902 samples = 22 value = [15, 7] 636->637 656 entropy = 0.0 samples = 1 value = [1, 0] 636->656 638 education <= 9.5 entropy = 0.918 samples = 21 value = [14, 7] 637->638 655 entropy = 0.0 samples = 1 value = [1, 0] 637->655 639 hours-per-week <= 48.5 entropy = 0.811 samples = 12 value = [9, 3] 638->639 648 hours-per-week <= 49.0 entropy = 0.991 samples = 9 value = [5, 4] 638->648 640 entropy = 1.0 samples = 2 value = [1, 1] 639->640 641 hours-per-week <= 49.5 entropy = 0.722 samples = 10 value = [8, 2] 639->641 642 entropy = 0.0 samples = 1 value = [1, 0] 641->642 643 age <= 30.5 entropy = 0.764 samples = 9 value = [7, 2] 641->643 644 entropy = 0.918 samples = 3 value = [2, 1] 643->644 645 age <= 31.5 entropy = 0.65 samples = 6 value = [5, 1] 643->645 646 entropy = 0.0 samples = 3 value = [3, 0] 645->646 647 entropy = 0.918 samples = 3 value = [2, 1] 645->647 649 entropy = 0.0 samples = 1 value = [1, 0] 648->649 650 age <= 30.5 entropy = 1.0 samples = 8 value = [4, 4] 648->650 651 entropy = 1.0 samples = 2 value = [1, 1] 650->651 652 age <= 31.5 entropy = 1.0 samples = 6 value = [3, 3] 650->652 653 entropy = 1.0 samples = 4 value = [2, 2] 652->653 654 entropy = 1.0 samples = 2 value = [1, 1] 652->654 659 workclass_Private <= 0.5 entropy = 0.993 samples = 31 value = [14, 17] 658->659 694 entropy = 0.0 samples = 1 value = [1, 0] 658->694 660 hours-per-week <= 62.5 entropy = 0.65 samples = 6 value = [1, 5] 659->660 665 hours-per-week <= 85.0 entropy = 0.999 samples = 25 value = [13, 12] 659->665 661 education <= 9.5 entropy = 0.918 samples = 3 value = [1, 2] 660->661 664 entropy = 0.0 samples = 3 value = [0, 3] 660->664 662 entropy = 0.0 samples = 1 value = [1, 0] 661->662 663 entropy = 0.0 samples = 2 value = [0, 2] 661->663 666 hours-per-week <= 76.0 entropy = 1.0 samples = 24 value = [12, 12] 665->666 693 entropy = 0.0 samples = 1 value = [1, 0] 665->693 667 hours-per-week <= 68.5 entropy = 0.999 samples = 23 value = [12, 11] 666->667 692 entropy = 0.0 samples = 1 value = [0, 1] 666->692 668 age <= 31.5 entropy = 1.0 samples = 22 value = [11, 11] 667->668 691 entropy = 0.0 samples = 1 value = [1, 0] 667->691 669 hours-per-week <= 55.5 entropy = 0.985 samples = 14 value = [8, 6] 668->669 682 education <= 10.5 entropy = 0.954 samples = 8 value = [3, 5] 668->682 670 age <= 30.5 entropy = 0.722 samples = 5 value = [4, 1] 669->670 675 age <= 30.5 entropy = 0.991 samples = 9 value = [4, 5] 669->675 671 entropy = 0.0 samples = 2 value = [2, 0] 670->671 672 education <= 9.5 entropy = 0.918 samples = 3 value = [2, 1] 670->672 673 entropy = 0.0 samples = 1 value = [1, 0] 672->673 674 entropy = 1.0 samples = 2 value = [1, 1] 672->674 676 education <= 9.5 entropy = 0.722 samples = 5 value = [1, 4] 675->676 679 education <= 9.5 entropy = 0.811 samples = 4 value = [3, 1] 675->679 677 entropy = 0.918 samples = 3 value = [1, 2] 676->677 678 entropy = 0.0 samples = 2 value = [0, 2] 676->678 680 entropy = 0.0 samples = 2 value = [2, 0] 679->680 681 entropy = 1.0 samples = 2 value = [1, 1] 679->681 683 education <= 9.5 entropy = 0.985 samples = 7 value = [3, 4] 682->683 690 entropy = 0.0 samples = 1 value = [0, 1] 682->690 684 hours-per-week <= 57.5 entropy = 0.811 samples = 4 value = [1, 3] 683->684 687 hours-per-week <= 57.5 entropy = 0.918 samples = 3 value = [2, 1] 683->687 685 entropy = 0.0 samples = 2 value = [0, 2] 684->685 686 entropy = 1.0 samples = 2 value = [1, 1] 684->686 688 entropy = 0.0 samples = 2 value = [2, 0] 687->688 689 entropy = 0.0 samples = 1 value = [0, 1] 687->689 696 entropy = 0.0 samples = 3 value = [0, 3] 695->696 697 workclass_Private <= 0.5 entropy = 0.999 samples = 74 value = [38, 36] 695->697 698 race_Black <= 0.5 entropy = 0.958 samples = 29 value = [18, 11] 697->698 731 hours-per-week <= 65.0 entropy = 0.991 samples = 45 value = [20, 25] 697->731 699 workclass_Public <= 0.5 entropy = 0.94 samples = 28 value = [18, 10] 698->699 730 entropy = 0.0 samples = 1 value = [0, 1] 698->730 700 race_Asian <= 0.5 entropy = 0.971 samples = 25 value = [15, 10] 699->700 729 entropy = 0.0 samples = 3 value = [3, 0] 699->729 701 age <= 33.5 entropy = 0.954 samples = 24 value = [15, 9] 700->701 728 entropy = 0.0 samples = 1 value = [0, 1] 700->728 702 entropy = 0.0 samples = 3 value = [3, 0] 701->702 703 race_White <= 0.5 entropy = 0.985 samples = 21 value = [12, 9] 701->703 704 entropy = 0.0 samples = 1 value = [1, 0] 703->704 705 education <= 10.5 entropy = 0.993 samples = 20 value = [11, 9] 703->705 706 hours-per-week <= 77.5 entropy = 0.998 samples = 19 value = [10, 9] 705->706 727 entropy = 0.0 samples = 1 value = [1, 0] 705->727 707 hours-per-week <= 73.5 entropy = 1.0 samples = 18 value = [9, 9] 706->707 726 entropy = 0.0 samples = 1 value = [1, 0] 706->726 708 education <= 9.5 entropy = 0.998 samples = 17 value = [9, 8] 707->708 725 entropy = 0.0 samples = 1 value = [0, 1] 707->725 709 hours-per-week <= 54.5 entropy = 0.918 samples = 9 value = [6, 3] 708->709 718 hours-per-week <= 66.0 entropy = 0.954 samples = 8 value = [3, 5] 708->718 710 hours-per-week <= 52.0 entropy = 1.0 samples = 4 value = [2, 2] 709->710 715 hours-per-week <= 57.5 entropy = 0.722 samples = 5 value = [4, 1] 709->715 711 age <= 34.5 entropy = 0.918 samples = 3 value = [2, 1] 710->711 714 entropy = 0.0 samples = 1 value = [0, 1] 710->714 712 entropy = 0.0 samples = 1 value = [1, 0] 711->712 713 entropy = 1.0 samples = 2 value = [1, 1] 711->713 716 entropy = 0.0 samples = 1 value = [1, 0] 715->716 717 entropy = 0.811 samples = 4 value = [3, 1] 715->717 719 age <= 34.5 entropy = 0.863 samples = 7 value = [2, 5] 718->719 724 entropy = 0.0 samples = 1 value = [1, 0] 718->724 720 entropy = 0.0 samples = 2 value = [0, 2] 719->720 721 hours-per-week <= 55.0 entropy = 0.971 samples = 5 value = [2, 3] 719->721 722 entropy = 1.0 samples = 2 value = [1, 1] 721->722 723 entropy = 0.918 samples = 3 value = [1, 2] 721->723 732 hours-per-week <= 57.0 entropy = 0.998 samples = 42 value = [20, 22] 731->732 763 entropy = 0.0 samples = 3 value = [0, 3] 731->763 733 hours-per-week <= 54.5 entropy = 0.971 samples = 30 value = [12, 18] 732->733 754 age <= 33.5 entropy = 0.918 samples = 12 value = [8, 4] 732->754 734 hours-per-week <= 52.0 entropy = 0.996 samples = 26 value = [12, 14] 733->734 753 entropy = 0.0 samples = 4 value = [0, 4] 733->753 735 education <= 10.5 entropy = 0.99 samples = 25 value = [11, 14] 734->735 752 entropy = 0.0 samples = 1 value = [1, 0] 734->752 736 education <= 9.5 entropy = 0.971 samples = 20 value = [8, 12] 735->736 749 age <= 34.5 entropy = 0.971 samples = 5 value = [3, 2] 735->749 737 race_White <= 0.5 entropy = 1.0 samples = 12 value = [6, 6] 736->737 744 age <= 34.5 entropy = 0.811 samples = 8 value = [2, 6] 736->744 738 entropy = 0.0 samples = 1 value = [1, 0] 737->738 739 age <= 33.5 entropy = 0.994 samples = 11 value = [5, 6] 737->739 740 entropy = 0.811 samples = 4 value = [1, 3] 739->740 741 age <= 34.5 entropy = 0.985 samples = 7 value = [4, 3] 739->741 742 entropy = 0.811 samples = 4 value = [3, 1] 741->742 743 entropy = 0.918 samples = 3 value = [1, 2] 741->743 745 entropy = 0.0 samples = 4 value = [0, 4] 744->745 746 race_Black <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 744->746 747 entropy = 0.918 samples = 3 value = [2, 1] 746->747 748 entropy = 0.0 samples = 1 value = [0, 1] 746->748 750 entropy = 0.0 samples = 2 value = [2, 0] 749->750 751 entropy = 0.918 samples = 3 value = [1, 2] 749->751 755 entropy = 0.0 samples = 4 value = [4, 0] 754->755 756 age <= 34.5 entropy = 1.0 samples = 8 value = [4, 4] 754->756 757 entropy = 0.0 samples = 3 value = [0, 3] 756->757 758 education <= 9.5 entropy = 0.722 samples = 5 value = [4, 1] 756->758 759 entropy = 0.0 samples = 2 value = [2, 0] 758->759 760 hours-per-week <= 59.0 entropy = 0.918 samples = 3 value = [2, 1] 758->760 761 entropy = 0.0 samples = 1 value = [1, 0] 760->761 762 entropy = 1.0 samples = 2 value = [1, 1] 760->762 765 education <= 9.5 entropy = 0.989 samples = 1566 value = [880, 686] 764->765 1874 workclass_Public <= 0.5 entropy = 0.791 samples = 101 value = [77, 24] 764->1874 766 race_Amer-Indian <= 0.5 entropy = 0.969 samples = 924 value = [557, 367] 765->766 1349 hours-per-week <= 43.5 entropy = 1.0 samples = 642 value = [323, 319] 765->1349 767 race_Hispanic <= 0.5 entropy = 0.972 samples = 912 value = [546, 366] 766->767 1346 age <= 58.0 entropy = 0.414 samples = 12 value = [11, 1] 766->1346 768 sex_Male <= 0.5 entropy = 0.973 samples = 908 value = [542, 366] 767->768 1345 entropy = 0.0 samples = 4 value = [4, 0] 767->1345 769 age <= 53.5 entropy = 0.913 samples = 125 value = [84, 41] 768->769 858 hours-per-week <= 61.5 entropy = 0.979 samples = 783 value = [458, 325] 768->858 770 workclass_Public <= 0.5 entropy = 0.979 samples = 89 value = [52, 37] 769->770 841 age <= 57.5 entropy = 0.503 samples = 36 value = [32, 4] 769->841 771 hours-per-week <= 41.5 entropy = 0.948 samples = 79 value = [50, 29] 770->771 836 age <= 41.0 entropy = 0.722 samples = 10 value = [2, 8] 770->836 772 workclass_Private <= 0.5 entropy = 0.986 samples = 65 value = [37, 28] 771->772 831 race_Black <= 0.5 entropy = 0.371 samples = 14 value = [13, 1] 771->831 773 age <= 42.5 entropy = 0.65 samples = 6 value = [1, 5] 772->773 778 race_Asian <= 0.5 entropy = 0.965 samples = 59 value = [36, 23] 772->778 774 age <= 39.0 entropy = 0.918 samples = 3 value = [1, 2] 773->774 777 entropy = 0.0 samples = 3 value = [0, 3] 773->777 775 entropy = 0.0 samples = 2 value = [0, 2] 774->775 776 entropy = 0.0 samples = 1 value = [1, 0] 774->776 779 race_White <= 0.5 entropy = 0.958 samples = 58 value = [36, 22] 778->779 830 entropy = 0.0 samples = 1 value = [0, 1] 778->830 780 age <= 45.5 entropy = 0.779 samples = 13 value = [10, 3] 779->780 787 age <= 52.5 entropy = 0.982 samples = 45 value = [26, 19] 779->787 781 entropy = 0.0 samples = 5 value = [5, 0] 780->781 782 age <= 48.0 entropy = 0.954 samples = 8 value = [5, 3] 780->782 783 entropy = 0.0 samples = 2 value = [0, 2] 782->783 784 age <= 50.0 entropy = 0.65 samples = 6 value = [5, 1] 782->784 785 entropy = 0.918 samples = 3 value = [2, 1] 784->785 786 entropy = 0.0 samples = 3 value = [3, 0] 784->786 788 age <= 51.0 entropy = 0.976 samples = 44 value = [26, 18] 787->788 829 entropy = 0.0 samples = 1 value = [0, 1] 787->829 789 age <= 45.5 entropy = 0.985 samples = 42 value = [24, 18] 788->789 828 entropy = 0.0 samples = 2 value = [2, 0] 788->828 790 age <= 44.5 entropy = 0.951 samples = 27 value = [17, 10] 789->790 817 hours-per-week <= 39.0 entropy = 0.997 samples = 15 value = [7, 8] 789->817 791 age <= 38.5 entropy = 0.961 samples = 26 value = [16, 10] 790->791 816 entropy = 0.0 samples = 1 value = [1, 0] 790->816 792 age <= 37.5 entropy = 0.863 samples = 7 value = [5, 2] 791->792 797 age <= 39.5 entropy = 0.982 samples = 19 value = [11, 8] 791->797 793 age <= 36.5 entropy = 1.0 samples = 4 value = [2, 2] 792->793 796 entropy = 0.0 samples = 3 value = [3, 0] 792->796 794 entropy = 0.918 samples = 3 value = [2, 1] 793->794 795 entropy = 0.0 samples = 1 value = [0, 1] 793->795 798 hours-per-week <= 38.0 entropy = 0.971 samples = 5 value = [2, 3] 797->798 801 hours-per-week <= 37.0 entropy = 0.94 samples = 14 value = [9, 5] 797->801 799 entropy = 0.0 samples = 1 value = [1, 0] 798->799 800 entropy = 0.811 samples = 4 value = [1, 3] 798->800 802 hours-per-week <= 35.5 entropy = 0.918 samples = 3 value = [1, 2] 801->802 807 age <= 40.5 entropy = 0.845 samples = 11 value = [8, 3] 801->807 803 age <= 41.0 entropy = 1.0 samples = 2 value = [1, 1] 802->803 806 entropy = 0.0 samples = 1 value = [0, 1] 802->806 804 entropy = 0.0 samples = 1 value = [0, 1] 803->804 805 entropy = 0.0 samples = 1 value = [1, 0] 803->805 808 entropy = 0.0 samples = 2 value = [2, 0] 807->808 809 hours-per-week <= 39.0 entropy = 0.918 samples = 9 value = [6, 3] 807->809 810 entropy = 0.0 samples = 1 value = [1, 0] 809->810 811 age <= 41.5 entropy = 0.954 samples = 8 value = [5, 3] 809->811 812 entropy = 1.0 samples = 2 value = [1, 1] 811->812 813 age <= 43.0 entropy = 0.918 samples = 6 value = [4, 2] 811->813 814 entropy = 0.811 samples = 4 value = [3, 1] 813->814 815 entropy = 1.0 samples = 2 value = [1, 1] 813->815 818 entropy = 0.0 samples = 1 value = [0, 1] 817->818 819 age <= 48.5 entropy = 1.0 samples = 14 value = [7, 7] 817->819 820 age <= 47.5 entropy = 0.991 samples = 9 value = [5, 4] 819->820 825 age <= 49.5 entropy = 0.971 samples = 5 value = [2, 3] 819->825 821 age <= 46.5 entropy = 1.0 samples = 8 value = [4, 4] 820->821 824 entropy = 0.0 samples = 1 value = [1, 0] 820->824 822 entropy = 1.0 samples = 6 value = [3, 3] 821->822 823 entropy = 1.0 samples = 2 value = [1, 1] 821->823 826 entropy = 0.0 samples = 1 value = [0, 1] 825->826 827 entropy = 1.0 samples = 4 value = [2, 2] 825->827 832 entropy = 0.0 samples = 12 value = [12, 0] 831->832 833 hours-per-week <= 49.5 entropy = 1.0 samples = 2 value = [1, 1] 831->833 834 entropy = 0.0 samples = 1 value = [1, 0] 833->834 835 entropy = 0.0 samples = 1 value = [0, 1] 833->835 837 age <= 37.0 entropy = 0.918 samples = 3 value = [2, 1] 836->837 840 entropy = 0.0 samples = 7 value = [0, 7] 836->840 838 entropy = 0.0 samples = 1 value = [1, 0] 837->838 839 entropy = 1.0 samples = 2 value = [1, 1] 837->839 842 entropy = 0.0 samples = 12 value = [12, 0] 841->842 843 hours-per-week <= 39.0 entropy = 0.65 samples = 24 value = [20, 4] 841->843 844 age <= 60.0 entropy = 1.0 samples = 6 value = [3, 3] 843->844 851 age <= 60.5 entropy = 0.31 samples = 18 value = [17, 1] 843->851 845 workclass_Private <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 844->845 850 entropy = 0.0 samples = 2 value = [2, 0] 844->850 846 entropy = 0.0 samples = 2 value = [0, 2] 845->846 847 hours-per-week <= 36.0 entropy = 1.0 samples = 2 value = [1, 1] 845->847 848 entropy = 0.0 samples = 1 value = [0, 1] 847->848 849 entropy = 0.0 samples = 1 value = [1, 0] 847->849 852 age <= 59.5 entropy = 0.469 samples = 10 value = [9, 1] 851->852 857 entropy = 0.0 samples = 8 value = [8, 0] 851->857 853 entropy = 0.0 samples = 6 value = [6, 0] 852->853 854 race_Black <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 852->854 855 entropy = 0.918 samples = 3 value = [2, 1] 854->855 856 entropy = 0.0 samples = 1 value = [1, 0] 854->856 859 hours-per-week <= 41.0 entropy = 0.983 samples = 743 value = [429, 314] 858->859 1312 age <= 52.5 entropy = 0.849 samples = 40 value = [29, 11] 858->1312 860 age <= 37.5 entropy = 0.963 samples = 462 value = [283, 179] 859->860 1069 hours-per-week <= 42.5 entropy = 0.999 samples = 281 value = [146, 135] 859->1069 861 race_White <= 0.5 entropy = 0.859 samples = 46 value = [33, 13] 860->861 874 age <= 59.5 entropy = 0.97 samples = 416 value = [250, 166] 860->874 862 entropy = 0.0 samples = 4 value = [4, 0] 861->862 863 hours-per-week <= 37.5 entropy = 0.893 samples = 42 value = [29, 13] 861->863 864 entropy = 0.0 samples = 1 value = [0, 1] 863->864 865 workclass_Public <= 0.5 entropy = 0.872 samples = 41 value = [29, 12] 863->865 866 workclass_Self-emp <= 0.5 entropy = 0.834 samples = 34 value = [25, 9] 865->866 871 age <= 36.5 entropy = 0.985 samples = 7 value = [4, 3] 865->871 867 age <= 36.5 entropy = 0.845 samples = 33 value = [24, 9] 866->867 870 entropy = 0.0 samples = 1 value = [1, 0] 866->870 868 entropy = 0.837 samples = 15 value = [11, 4] 867->868 869 entropy = 0.852 samples = 18 value = [13, 5] 867->869 872 entropy = 1.0 samples = 4 value = [2, 2] 871->872 873 entropy = 0.918 samples = 3 value = [2, 1] 871->873 875 race_Asian <= 0.5 entropy = 0.976 samples = 387 value = [229, 158] 874->875 1052 race_White <= 0.5 entropy = 0.85 samples = 29 value = [21, 8] 874->1052 876 age <= 52.5 entropy = 0.973 samples = 382 value = [228, 154] 875->876 1047 workclass_Self-emp <= 0.5 entropy = 0.722 samples = 5 value = [1, 4] 875->1047 877 hours-per-week <= 37.0 entropy = 0.958 samples = 295 value = [183, 112] 876->877 1004 race_White <= 0.5 entropy = 0.999 samples = 87 value = [45, 42] 876->1004 878 age <= 41.5 entropy = 0.722 samples = 10 value = [8, 2] 877->878 885 age <= 39.5 entropy = 0.962 samples = 285 value = [175, 110] 877->885 879 entropy = 0.0 samples = 4 value = [4, 0] 878->879 880 age <= 44.5 entropy = 0.918 samples = 6 value = [4, 2] 878->880 881 age <= 42.5 entropy = 0.918 samples = 3 value = [1, 2] 880->881 884 entropy = 0.0 samples = 3 value = [3, 0] 880->884 882 entropy = 0.0 samples = 1 value = [0, 1] 881->882 883 entropy = 1.0 samples = 2 value = [1, 1] 881->883 886 race_White <= 0.5 entropy = 0.995 samples = 63 value = [34, 29] 885->886 905 workclass_Private <= 0.5 entropy = 0.947 samples = 222 value = [141, 81] 885->905 887 workclass_Private <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 886->887 892 hours-per-week <= 39.0 entropy = 0.999 samples = 58 value = [30, 28] 886->892 888 entropy = 0.0 samples = 1 value = [1, 0] 887->888 889 age <= 38.5 entropy = 0.811 samples = 4 value = [3, 1] 887->889 890 entropy = 0.918 samples = 3 value = [2, 1] 889->890 891 entropy = 0.0 samples = 1 value = [1, 0] 889->891 893 entropy = 0.0 samples = 1 value = [1, 0] 892->893 894 age <= 38.5 entropy = 1.0 samples = 57 value = [29, 28] 892->894 895 workclass_Self-emp <= 0.5 entropy = 0.999 samples = 33 value = [16, 17] 894->895 900 workclass_Private <= 0.5 entropy = 0.995 samples = 24 value = [13, 11] 894->900 896 workclass_Public <= 0.5 entropy = 0.996 samples = 26 value = [12, 14] 895->896 899 entropy = 0.985 samples = 7 value = [4, 3] 895->899 897 entropy = 0.998 samples = 19 value = [9, 10] 896->897 898 entropy = 0.985 samples = 7 value = [3, 4] 896->898 901 workclass_Public <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 900->901 904 entropy = 0.991 samples = 18 value = [10, 8] 900->904 902 entropy = 1.0 samples = 4 value = [2, 2] 901->902 903 entropy = 1.0 samples = 2 value = [1, 1] 901->903 906 age <= 46.5 entropy = 0.983 samples = 66 value = [38, 28] 905->906 959 age <= 49.5 entropy = 0.925 samples = 156 value = [103, 53] 905->959 907 age <= 40.5 entropy = 0.868 samples = 38 value = [27, 11] 906->907 936 age <= 51.5 entropy = 0.967 samples = 28 value = [11, 17] 906->936 908 entropy = 0.0 samples = 4 value = [4, 0] 907->908 909 age <= 42.5 entropy = 0.908 samples = 34 value = [23, 11] 907->909 910 age <= 41.5 entropy = 1.0 samples = 10 value = [5, 5] 909->910 917 hours-per-week <= 39.0 entropy = 0.811 samples = 24 value = [18, 6] 909->917 911 hours-per-week <= 39.0 entropy = 0.991 samples = 9 value = [5, 4] 910->911 916 entropy = 0.0 samples = 1 value = [0, 1] 910->916 912 entropy = 0.0 samples = 1 value = [1, 0] 911->912 913 workclass_Public <= 0.5 entropy = 1.0 samples = 8 value = [4, 4] 911->913 914 entropy = 0.918 samples = 3 value = [1, 2] 913->914 915 entropy = 0.971 samples = 5 value = [3, 2] 913->915 918 entropy = 0.0 samples = 1 value = [0, 1] 917->918 919 age <= 43.5 entropy = 0.755 samples = 23 value = [18, 5] 917->919 920 workclass_Public <= 0.5 entropy = 0.503 samples = 9 value = [8, 1] 919->920 925 age <= 45.5 entropy = 0.863 samples = 14 value = [10, 4] 919->925 921 entropy = 0.0 samples = 3 value = [3, 0] 920->921 922 race_Black <= 0.5 entropy = 0.65 samples = 6 value = [5, 1] 920->922 923 entropy = 0.722 samples = 5 value = [4, 1] 922->923 924 entropy = 0.0 samples = 1 value = [1, 0] 922->924 926 workclass_Self-emp <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 925->926 933 workclass_Public <= 0.5 entropy = 0.592 samples = 7 value = [6, 1] 925->933 927 age <= 44.5 entropy = 0.918 samples = 3 value = [2, 1] 926->927 930 age <= 44.5 entropy = 1.0 samples = 4 value = [2, 2] 926->930 928 entropy = 0.0 samples = 1 value = [1, 0] 927->928 929 entropy = 1.0 samples = 2 value = [1, 1] 927->929 931 entropy = 0.0 samples = 1 value = [0, 1] 930->931 932 entropy = 0.918 samples = 3 value = [2, 1] 930->932 934 entropy = 0.0 samples = 1 value = [1, 0] 933->934 935 entropy = 0.65 samples = 6 value = [5, 1] 933->935 937 workclass_Self-emp <= 0.5 entropy = 0.904 samples = 25 value = [8, 17] 936->937 958 entropy = 0.0 samples = 3 value = [3, 0] 936->958 938 age <= 47.5 entropy = 0.949 samples = 19 value = [7, 12] 937->938 953 age <= 48.5 entropy = 0.65 samples = 6 value = [1, 5] 937->953 939 race_White <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] 938->939 942 race_Black <= 0.5 entropy = 0.961 samples = 13 value = [5, 8] 938->942 940 entropy = 0.0 samples = 2 value = [0, 2] 939->940 941 entropy = 1.0 samples = 4 value = [2, 2] 939->941 943 age <= 48.5 entropy = 0.881 samples = 10 value = [3, 7] 942->943 950 age <= 49.5 entropy = 0.918 samples = 3 value = [2, 1] 942->950 944 entropy = 1.0 samples = 2 value = [1, 1] 943->944 945 age <= 50.5 entropy = 0.811 samples = 8 value = [2, 6] 943->945 946 age <= 49.5 entropy = 0.722 samples = 5 value = [1, 4] 945->946 949 entropy = 0.918 samples = 3 value = [1, 2] 945->949 947 entropy = 0.811 samples = 4 value = [1, 3] 946->947 948 entropy = 0.0 samples = 1 value = [0, 1] 946->948 951 entropy = 0.0 samples = 1 value = [1, 0] 950->951 952 entropy = 1.0 samples = 2 value = [1, 1] 950->952 954 entropy = 0.0 samples = 3 value = [0, 3] 953->954 955 age <= 49.5 entropy = 0.918 samples = 3 value = [1, 2] 953->955 956 entropy = 1.0 samples = 2 value = [1, 1] 955->956 957 entropy = 0.0 samples = 1 value = [0, 1] 955->957 960 age <= 46.5 entropy = 0.907 samples = 121 value = [82, 39] 959->960 993 age <= 50.5 entropy = 0.971 samples = 35 value = [21, 14] 959->993 961 race_White <= 0.5 entropy = 0.936 samples = 88 value = [57, 31] 960->961 984 race_Black <= 0.5 entropy = 0.799 samples = 33 value = [25, 8] 960->984 962 age <= 42.5 entropy = 1.0 samples = 12 value = [6, 6] 961->962 971 age <= 42.5 entropy = 0.914 samples = 76 value = [51, 25] 961->971 963 age <= 40.5 entropy = 0.722 samples = 5 value = [1, 4] 962->963 966 age <= 45.5 entropy = 0.863 samples = 7 value = [5, 2] 962->966 964 entropy = 1.0 samples = 2 value = [1, 1] 963->964 965 entropy = 0.0 samples = 3 value = [0, 3] 963->965 967 age <= 43.5 entropy = 0.65 samples = 6 value = [5, 1] 966->967 970 entropy = 0.0 samples = 1 value = [0, 1] 966->970 968 entropy = 1.0 samples = 2 value = [1, 1] 967->968 969 entropy = 0.0 samples = 4 value = [4, 0] 967->969 972 age <= 40.5 entropy = 0.811 samples = 36 value = [27, 9] 971->972 977 age <= 44.5 entropy = 0.971 samples = 40 value = [24, 16] 971->977 973 entropy = 0.971 samples = 10 value = [6, 4] 972->973 974 age <= 41.5 entropy = 0.706 samples = 26 value = [21, 5] 972->974 975 entropy = 0.787 samples = 17 value = [13, 4] 974->975 976 entropy = 0.503 samples = 9 value = [8, 1] 974->976 978 age <= 43.5 entropy = 0.966 samples = 23 value = [14, 9] 977->978 981 age <= 45.5 entropy = 0.977 samples = 17 value = [10, 7] 977->981 979 entropy = 0.971 samples = 10 value = [6, 4] 978->979 980 entropy = 0.961 samples = 13 value = [8, 5] 978->980 982 entropy = 0.985 samples = 7 value = [4, 3] 981->982 983 entropy = 0.971 samples = 10 value = [6, 4] 981->983 985 hours-per-week <= 39.0 entropy = 0.837 samples = 30 value = [22, 8] 984->985 992 entropy = 0.0 samples = 3 value = [3, 0] 984->992 986 entropy = 0.0 samples = 1 value = [1, 0] 985->986 987 age <= 47.5 entropy = 0.85 samples = 29 value = [21, 8] 985->987 988 entropy = 0.845 samples = 11 value = [8, 3] 987->988 989 age <= 48.5 entropy = 0.852 samples = 18 value = [13, 5] 987->989 990 entropy = 0.863 samples = 7 value = [5, 2] 989->990 991 entropy = 0.845 samples = 11 value = [8, 3] 989->991 994 race_Black <= 0.5 entropy = 0.971 samples = 10 value = [4, 6] 993->994 999 age <= 51.5 entropy = 0.904 samples = 25 value = [17, 8] 993->999 995 hours-per-week <= 39.0 entropy = 0.918 samples = 9 value = [3, 6] 994->995 998 entropy = 0.0 samples = 1 value = [1, 0] 994->998 996 entropy = 0.0 samples = 1 value = [0, 1] 995->996 997 entropy = 0.954 samples = 8 value = [3, 5] 995->997 1000 race_White <= 0.5 entropy = 0.896 samples = 16 value = [11, 5] 999->1000 1003 entropy = 0.918 samples = 9 value = [6, 3] 999->1003 1001 entropy = 0.918 samples = 3 value = [2, 1] 1000->1001 1002 entropy = 0.89 samples = 13 value = [9, 4] 1000->1002 1005 age <= 55.5 entropy = 0.811 samples = 8 value = [6, 2] 1004->1005 1010 hours-per-week <= 36.5 entropy = 1.0 samples = 79 value = [39, 40] 1004->1010 1006 entropy = 0.0 samples = 5 value = [5, 0] 1005->1006 1007 age <= 58.5 entropy = 0.918 samples = 3 value = [1, 2] 1005->1007 1008 entropy = 0.0 samples = 2 value = [0, 2] 1007->1008 1009 entropy = 0.0 samples = 1 value = [1, 0] 1007->1009 1011 workclass_Public <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 1010->1011 1014 age <= 56.5 entropy = 0.999 samples = 75 value = [36, 39] 1010->1014 1012 entropy = 0.0 samples = 3 value = [3, 0] 1011->1012 1013 entropy = 0.0 samples = 1 value = [0, 1] 1011->1013 1015 age <= 53.5 entropy = 0.989 samples = 41 value = [18, 23] 1014->1015 1032 age <= 58.5 entropy = 0.998 samples = 34 value = [18, 16] 1014->1032 1016 workclass_Public <= 0.5 entropy = 0.997 samples = 15 value = [8, 7] 1015->1016 1023 workclass_Public <= 0.5 entropy = 0.961 samples = 26 value = [10, 16] 1015->1023 1017 hours-per-week <= 39.0 entropy = 0.996 samples = 13 value = [6, 7] 1016->1017 1022 entropy = 0.0 samples = 2 value = [2, 0] 1016->1022 1018 entropy = 0.0 samples = 1 value = [1, 0] 1017->1018 1019 workclass_Self-emp <= 0.5 entropy = 0.98 samples = 12 value = [5, 7] 1017->1019 1020 entropy = 0.994 samples = 11 value = [5, 6] 1019->1020 1021 entropy = 0.0 samples = 1 value = [0, 1] 1019->1021 1024 workclass_Self-emp <= 0.5 entropy = 0.994 samples = 22 value = [10, 12] 1023->1024 1031 entropy = 0.0 samples = 4 value = [0, 4] 1023->1031 1025 age <= 55.5 entropy = 0.985 samples = 21 value = [9, 12] 1024->1025 1030 entropy = 0.0 samples = 1 value = [1, 0] 1024->1030 1026 age <= 54.5 entropy = 0.971 samples = 15 value = [6, 9] 1025->1026 1029 entropy = 1.0 samples = 6 value = [3, 3] 1025->1029 1027 entropy = 0.985 samples = 7 value = [3, 4] 1026->1027 1028 entropy = 0.954 samples = 8 value = [3, 5] 1026->1028 1033 age <= 57.5 entropy = 0.959 samples = 21 value = [13, 8] 1032->1033 1042 workclass_Self-emp <= 0.5 entropy = 0.961 samples = 13 value = [5, 8] 1032->1042 1034 workclass_Private <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 1033->1034 1037 workclass_Self-emp <= 0.5 entropy = 0.94 samples = 14 value = [9, 5] 1033->1037 1035 entropy = 0.0 samples = 1 value = [1, 0] 1034->1035 1036 entropy = 1.0 samples = 6 value = [3, 3] 1034->1036 1038 workclass_Public <= 0.5 entropy = 0.918 samples = 12 value = [8, 4] 1037->1038 1041 entropy = 1.0 samples = 2 value = [1, 1] 1037->1041 1039 entropy = 0.918 samples = 9 value = [6, 3] 1038->1039 1040 entropy = 0.918 samples = 3 value = [2, 1] 1038->1040 1043 workclass_Public <= 0.5 entropy = 0.994 samples = 11 value = [5, 6] 1042->1043 1046 entropy = 0.0 samples = 2 value = [0, 2] 1042->1046 1044 entropy = 0.954 samples = 8 value = [3, 5] 1043->1044 1045 entropy = 0.918 samples = 3 value = [2, 1] 1043->1045 1048 entropy = 0.0 samples = 3 value = [0, 3] 1047->1048 1049 age <= 41.5 entropy = 1.0 samples = 2 value = [1, 1] 1047->1049 1050 entropy = 0.0 samples = 1 value = [0, 1] 1049->1050 1051 entropy = 0.0 samples = 1 value = [1, 0] 1049->1051 1053 entropy = 0.0 samples = 2 value = [2, 0] 1052->1053 1054 workclass_Public <= 0.5 entropy = 0.877 samples = 27 value = [19, 8] 1052->1054 1055 age <= 61.5 entropy = 0.904 samples = 25 value = [17, 8] 1054->1055 1068 entropy = 0.0 samples = 2 value = [2, 0] 1054->1068 1056 hours-per-week <= 37.5 entropy = 0.863 samples = 21 value = [15, 6] 1055->1056 1065 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 1055->1065 1057 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 1056->1057 1060 workclass_Private <= 0.5 entropy = 0.831 samples = 19 value = [14, 5] 1056->1060 1058 entropy = 0.0 samples = 1 value = [1, 0] 1057->1058 1059 entropy = 0.0 samples = 1 value = [0, 1] 1057->1059 1061 entropy = 0.0 samples = 2 value = [2, 0] 1060->1061 1062 age <= 60.5 entropy = 0.874 samples = 17 value = [12, 5] 1060->1062 1063 entropy = 0.946 samples = 11 value = [7, 4] 1062->1063 1064 entropy = 0.65 samples = 6 value = [5, 1] 1062->1064 1066 entropy = 0.918 samples = 3 value = [1, 2] 1065->1066 1067 entropy = 0.0 samples = 1 value = [1, 0] 1065->1067 1070 entropy = 0.0 samples = 5 value = [0, 5] 1069->1070 1071 age <= 61.5 entropy = 0.998 samples = 276 value = [146, 130] 1069->1071 1072 age <= 43.5 entropy = 0.997 samples = 274 value = [146, 128] 1071->1072 1311 entropy = 0.0 samples = 2 value = [0, 2] 1071->1311 1073 age <= 40.5 entropy = 0.979 samples = 111 value = [65, 46] 1072->1073 1156 age <= 54.5 entropy = 1.0 samples = 163 value = [81, 82] 1072->1156 1074 hours-per-week <= 54.5 entropy = 0.998 samples = 84 value = [44, 40] 1073->1074 1135 hours-per-week <= 43.5 entropy = 0.764 samples = 27 value = [21, 6] 1073->1135 1075 hours-per-week <= 51.5 entropy = 0.999 samples = 60 value = [29, 31] 1074->1075 1118 hours-per-week <= 58.0 entropy = 0.954 samples = 24 value = [15, 9] 1074->1118 1076 age <= 39.5 entropy = 1.0 samples = 58 value = [29, 29] 1075->1076 1117 entropy = 0.0 samples = 2 value = [0, 2] 1075->1117 1077 age <= 38.5 entropy = 0.998 samples = 51 value = [24, 27] 1076->1077 1112 hours-per-week <= 47.5 entropy = 0.863 samples = 7 value = [5, 2] 1076->1112 1078 workclass_Public <= 0.5 entropy = 0.996 samples = 43 value = [23, 20] 1077->1078 1109 workclass_Self-emp <= 0.5 entropy = 0.544 samples = 8 value = [1, 7] 1077->1109 1079 race_Asian <= 0.5 entropy = 0.993 samples = 42 value = [23, 19] 1078->1079 1108 entropy = 0.0 samples = 1 value = [0, 1] 1078->1108 1080 hours-per-week <= 45.5 entropy = 0.996 samples = 41 value = [22, 19] 1079->1080 1107 entropy = 0.0 samples = 1 value = [1, 0] 1079->1107 1081 workclass_Self-emp <= 0.5 entropy = 0.949 samples = 19 value = [12, 7] 1080->1081 1092 workclass_Private <= 0.5 entropy = 0.994 samples = 22 value = [10, 12] 1080->1092 1082 age <= 37.5 entropy = 0.918 samples = 18 value = [12, 6] 1081->1082 1091 entropy = 0.0 samples = 1 value = [0, 1] 1081->1091 1083 hours-per-week <= 44.5 entropy = 0.994 samples = 11 value = [6, 5] 1082->1083 1088 hours-per-week <= 44.5 entropy = 0.592 samples = 7 value = [6, 1] 1082->1088 1084 entropy = 0.0 samples = 2 value = [0, 2] 1083->1084 1085 age <= 36.5 entropy = 0.918 samples = 9 value = [6, 3] 1083->1085 1086 entropy = 0.722 samples = 5 value = [4, 1] 1085->1086 1087 entropy = 1.0 samples = 4 value = [2, 2] 1085->1087 1089 entropy = 0.0 samples = 2 value = [2, 0] 1088->1089 1090 entropy = 0.722 samples = 5 value = [4, 1] 1088->1090 1093 age <= 37.0 entropy = 0.722 samples = 5 value = [4, 1] 1092->1093 1096 hours-per-week <= 50.5 entropy = 0.937 samples = 17 value = [6, 11] 1092->1096 1094 entropy = 0.0 samples = 3 value = [3, 0] 1093->1094 1095 entropy = 1.0 samples = 2 value = [1, 1] 1093->1095 1097 age <= 37.5 entropy = 0.896 samples = 16 value = [5, 11] 1096->1097 1106 entropy = 0.0 samples = 1 value = [1, 0] 1096->1106 1098 hours-per-week <= 49.0 entropy = 0.544 samples = 8 value = [1, 7] 1097->1098 1103 hours-per-week <= 49.5 entropy = 1.0 samples = 8 value = [4, 4] 1097->1103 1099 hours-per-week <= 47.0 entropy = 1.0 samples = 2 value = [1, 1] 1098->1099 1102 entropy = 0.0 samples = 6 value = [0, 6] 1098->1102 1100 entropy = 0.0 samples = 1 value = [0, 1] 1099->1100 1101 entropy = 0.0 samples = 1 value = [1, 0] 1099->1101 1104 entropy = 0.0 samples = 3 value = [0, 3] 1103->1104 1105 entropy = 0.722 samples = 5 value = [4, 1] 1103->1105 1110 entropy = 0.0 samples = 6 value = [0, 6] 1109->1110 1111 entropy = 1.0 samples = 2 value = [1, 1] 1109->1111 1113 entropy = 1.0 samples = 2 value = [1, 1] 1112->1113 1114 workclass_Self-emp <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 1112->1114 1115 entropy = 0.0 samples = 2 value = [2, 0] 1114->1115 1116 entropy = 0.918 samples = 3 value = [2, 1] 1114->1116 1119 entropy = 0.0 samples = 4 value = [4, 0] 1118->1119 1120 age <= 37.5 entropy = 0.993 samples = 20 value = [11, 9] 1118->1120 1121 workclass_Private <= 0.5 entropy = 0.971 samples = 10 value = [4, 6] 1120->1121 1128 workclass_Private <= 0.5 entropy = 0.881 samples = 10 value = [7, 3] 1120->1128 1122 age <= 36.5 entropy = 0.811 samples = 4 value = [1, 3] 1121->1122 1125 age <= 36.5 entropy = 1.0 samples = 6 value = [3, 3] 1121->1125 1123 entropy = 0.0 samples = 1 value = [0, 1] 1122->1123 1124 entropy = 0.918 samples = 3 value = [1, 2] 1122->1124 1126 entropy = 0.971 samples = 5 value = [3, 2] 1125->1126 1127 entropy = 0.0 samples = 1 value = [0, 1] 1125->1127 1129 entropy = 0.0 samples = 5 value = [5, 0] 1128->1129 1130 age <= 39.5 entropy = 0.971 samples = 5 value = [2, 3] 1128->1130 1131 race_Black <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 1130->1131 1134 entropy = 0.0 samples = 2 value = [0, 2] 1130->1134 1132 entropy = 0.0 samples = 2 value = [2, 0] 1131->1132 1133 entropy = 0.0 samples = 1 value = [0, 1] 1131->1133 1136 entropy = 0.0 samples = 1 value = [0, 1] 1135->1136 1137 workclass_Private <= 0.5 entropy = 0.706 samples = 26 value = [21, 5] 1135->1137 1138 entropy = 0.0 samples = 7 value = [7, 0] 1137->1138 1139 hours-per-week <= 44.5 entropy = 0.831 samples = 19 value = [14, 5] 1137->1139 1140 entropy = 0.0 samples = 2 value = [2, 0] 1139->1140 1141 hours-per-week <= 55.0 entropy = 0.874 samples = 17 value = [12, 5] 1139->1141 1142 hours-per-week <= 48.5 entropy = 0.918 samples = 15 value = [10, 5] 1141->1142 1155 entropy = 0.0 samples = 2 value = [2, 0] 1141->1155 1143 age <= 41.5 entropy = 0.764 samples = 9 value = [7, 2] 1142->1143 1148 age <= 42.5 entropy = 1.0 samples = 6 value = [3, 3] 1142->1148 1144 entropy = 0.0 samples = 3 value = [3, 0] 1143->1144 1145 hours-per-week <= 46.5 entropy = 0.918 samples = 6 value = [4, 2] 1143->1145 1146 entropy = 0.918 samples = 3 value = [2, 1] 1145->1146 1147 entropy = 0.918 samples = 3 value = [2, 1] 1145->1147 1149 hours-per-week <= 49.5 entropy = 0.971 samples = 5 value = [2, 3] 1148->1149 1154 entropy = 0.0 samples = 1 value = [1, 0] 1148->1154 1150 entropy = 0.0 samples = 1 value = [0, 1] 1149->1150 1151 age <= 41.5 entropy = 1.0 samples = 4 value = [2, 2] 1149->1151 1152 entropy = 1.0 samples = 2 value = [1, 1] 1151->1152 1153 entropy = 1.0 samples = 2 value = [1, 1] 1151->1153 1157 workclass_Public <= 0.5 entropy = 0.996 samples = 110 value = [51, 59] 1156->1157 1256 workclass_Public <= 0.5 entropy = 0.987 samples = 53 value = [30, 23] 1156->1256 1158 hours-per-week <= 43.5 entropy = 0.998 samples = 105 value = [50, 55] 1157->1158 1253 age <= 51.5 entropy = 0.722 samples = 5 value = [1, 4] 1157->1253 1159 entropy = 0.0 samples = 2 value = [2, 0] 1158->1159 1160 hours-per-week <= 44.5 entropy = 0.997 samples = 103 value = [48, 55] 1158->1160 1161 entropy = 0.0 samples = 2 value = [0, 2] 1160->1161 1162 hours-per-week <= 45.5 entropy = 0.998 samples = 101 value = [48, 53] 1160->1162 1163 workclass_Self-emp <= 0.5 entropy = 0.934 samples = 20 value = [7, 13] 1162->1163 1182 race_Asian <= 0.5 entropy = 1.0 samples = 81 value = [41, 40] 1162->1182 1164 age <= 52.5 entropy = 0.989 samples = 16 value = [7, 9] 1163->1164 1181 entropy = 0.0 samples = 4 value = [0, 4] 1163->1181 1165 age <= 45.5 entropy = 0.971 samples = 15 value = [6, 9] 1164->1165 1180 entropy = 0.0 samples = 1 value = [1, 0] 1164->1180 1166 age <= 44.5 entropy = 0.971 samples = 5 value = [3, 2] 1165->1166 1169 age <= 46.5 entropy = 0.881 samples = 10 value = [3, 7] 1165->1169 1167 entropy = 0.918 samples = 3 value = [1, 2] 1166->1167 1168 entropy = 0.0 samples = 2 value = [2, 0] 1166->1168 1170 entropy = 0.0 samples = 1 value = [0, 1] 1169->1170 1171 age <= 50.5 entropy = 0.918 samples = 9 value = [3, 6] 1169->1171 1172 age <= 49.5 entropy = 0.954 samples = 8 value = [3, 5] 1171->1172 1179 entropy = 0.0 samples = 1 value = [0, 1] 1171->1179 1173 age <= 48.5 entropy = 0.918 samples = 6 value = [2, 4] 1172->1173 1178 entropy = 1.0 samples = 2 value = [1, 1] 1172->1178 1174 age <= 47.5 entropy = 0.971 samples = 5 value = [2, 3] 1173->1174 1177 entropy = 0.0 samples = 1 value = [0, 1] 1173->1177 1175 entropy = 0.918 samples = 3 value = [1, 2] 1174->1175 1176 entropy = 1.0 samples = 2 value = [1, 1] 1174->1176 1183 age <= 51.5 entropy = 1.0 samples = 80 value = [41, 39] 1182->1183 1252 entropy = 0.0 samples = 1 value = [0, 1] 1182->1252 1184 hours-per-week <= 46.5 entropy = 0.994 samples = 64 value = [35, 29] 1183->1184 1241 race_Black <= 0.5 entropy = 0.954 samples = 16 value = [6, 10] 1183->1241 1185 entropy = 0.0 samples = 1 value = [1, 0] 1184->1185 1186 hours-per-week <= 47.5 entropy = 0.995 samples = 63 value = [34, 29] 1184->1186 1187 entropy = 0.0 samples = 1 value = [0, 1] 1186->1187 1188 age <= 50.5 entropy = 0.993 samples = 62 value = [34, 28] 1186->1188 1189 age <= 44.5 entropy = 0.998 samples = 55 value = [29, 26] 1188->1189 1234 hours-per-week <= 54.5 entropy = 0.863 samples = 7 value = [5, 2] 1188->1234 1190 hours-per-week <= 57.5 entropy = 0.811 samples = 4 value = [3, 1] 1189->1190 1193 hours-per-week <= 51.0 entropy = 1.0 samples = 51 value = [26, 25] 1189->1193 1191 entropy = 0.0 samples = 1 value = [1, 0] 1190->1191 1192 entropy = 0.918 samples = 3 value = [2, 1] 1190->1192 1194 age <= 46.5 entropy = 0.983 samples = 33 value = [19, 14] 1193->1194 1217 hours-per-week <= 59.0 entropy = 0.964 samples = 18 value = [7, 11] 1193->1217 1195 age <= 45.5 entropy = 0.65 samples = 6 value = [5, 1] 1194->1195 1198 age <= 47.5 entropy = 0.999 samples = 27 value = [14, 13] 1194->1198 1196 entropy = 1.0 samples = 2 value = [1, 1] 1195->1196 1197 entropy = 0.0 samples = 4 value = [4, 0] 1195->1197 1199 workclass_Private <= 0.5 entropy = 0.98 samples = 12 value = [5, 7] 1198->1199 1204 age <= 48.5 entropy = 0.971 samples = 15 value = [9, 6] 1198->1204 1200 entropy = 0.0 samples = 2 value = [0, 2] 1199->1200 1201 hours-per-week <= 49.0 entropy = 1.0 samples = 10 value = [5, 5] 1199->1201 1202 entropy = 0.918 samples = 3 value = [2, 1] 1201->1202 1203 entropy = 0.985 samples = 7 value = [3, 4] 1201->1203 1205 hours-per-week <= 49.0 entropy = 0.722 samples = 5 value = [4, 1] 1204->1205 1210 hours-per-week <= 49.0 entropy = 1.0 samples = 10 value = [5, 5] 1204->1210 1206 entropy = 0.0 samples = 1 value = [1, 0] 1205->1206 1207 workclass_Private <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 1205->1207 1208 entropy = 0.0 samples = 1 value = [1, 0] 1207->1208 1209 entropy = 0.918 samples = 3 value = [2, 1] 1207->1209 1211 entropy = 0.0 samples = 1 value = [0, 1] 1210->1211 1212 age <= 49.5 entropy = 0.991 samples = 9 value = [5, 4] 1210->1212 1213 entropy = 0.0 samples = 1 value = [1, 0] 1212->1213 1214 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 8 value = [4, 4] 1212->1214 1215 entropy = 0.971 samples = 5 value = [2, 3] 1214->1215 1216 entropy = 0.918 samples = 3 value = [2, 1] 1214->1216 1218 age <= 46.5 entropy = 0.592 samples = 7 value = [1, 6] 1217->1218 1223 age <= 46.5 entropy = 0.994 samples = 11 value = [6, 5] 1217->1223 1219 entropy = 0.0 samples = 3 value = [0, 3] 1218->1219 1220 age <= 48.0 entropy = 0.811 samples = 4 value = [1, 3] 1218->1220 1221 entropy = 1.0 samples = 2 value = [1, 1] 1220->1221 1222 entropy = 0.0 samples = 2 value = [0, 2] 1220->1222 1224 age <= 45.5 entropy = 0.918 samples = 3 value = [1, 2] 1223->1224 1227 age <= 47.5 entropy = 0.954 samples = 8 value = [5, 3] 1223->1227 1225 entropy = 1.0 samples = 2 value = [1, 1] 1224->1225 1226 entropy = 0.0 samples = 1 value = [0, 1] 1224->1226 1228 entropy = 0.0 samples = 2 value = [2, 0] 1227->1228 1229 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 1227->1229 1230 entropy = 0.0 samples = 2 value = [0, 2] 1229->1230 1231 age <= 48.5 entropy = 0.811 samples = 4 value = [3, 1] 1229->1231 1232 entropy = 0.0 samples = 1 value = [0, 1] 1231->1232 1233 entropy = 0.0 samples = 3 value = [3, 0] 1231->1233 1235 entropy = 0.0 samples = 3 value = [3, 0] 1234->1235 1236 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 1234->1236 1237 hours-per-week <= 57.5 entropy = 0.918 samples = 3 value = [1, 2] 1236->1237 1240 entropy = 0.0 samples = 1 value = [1, 0] 1236->1240 1238 entropy = 0.0 samples = 1 value = [0, 1] 1237->1238 1239 entropy = 1.0 samples = 2 value = [1, 1] 1237->1239 1242 hours-per-week <= 49.0 entropy = 0.918 samples = 15 value = [5, 10] 1241->1242 1251 entropy = 0.0 samples = 1 value = [1, 0] 1241->1251 1243 entropy = 0.0 samples = 1 value = [1, 0] 1242->1243 1244 hours-per-week <= 53.5 entropy = 0.863 samples = 14 value = [4, 10] 1242->1244 1245 age <= 52.5 entropy = 0.544 samples = 8 value = [1, 7] 1244->1245 1248 age <= 52.5 entropy = 1.0 samples = 6 value = [3, 3] 1244->1248 1246 entropy = 0.918 samples = 3 value = [1, 2] 1245->1246 1247 entropy = 0.0 samples = 5 value = [0, 5] 1245->1247 1249 entropy = 0.0 samples = 3 value = [0, 3] 1248->1249 1250 entropy = 0.0 samples = 3 value = [3, 0] 1248->1250 1254 entropy = 0.0 samples = 4 value = [0, 4] 1253->1254 1255 entropy = 0.0 samples = 1 value = [1, 0] 1253->1255 1257 hours-per-week <= 44.5 entropy = 0.995 samples = 50 value = [27, 23] 1256->1257 1310 entropy = 0.0 samples = 3 value = [3, 0] 1256->1310 1258 entropy = 0.0 samples = 2 value = [0, 2] 1257->1258 1259 race_White <= 0.5 entropy = 0.989 samples = 48 value = [27, 21] 1257->1259 1260 hours-per-week <= 56.0 entropy = 0.722 samples = 5 value = [4, 1] 1259->1260 1263 hours-per-week <= 57.5 entropy = 0.996 samples = 43 value = [23, 20] 1259->1263 1261 entropy = 0.0 samples = 4 value = [4, 0] 1260->1261 1262 entropy = 0.0 samples = 1 value = [0, 1] 1260->1262 1264 hours-per-week <= 51.0 entropy = 0.999 samples = 31 value = [15, 16] 1263->1264 1299 age <= 59.5 entropy = 0.918 samples = 12 value = [8, 4] 1263->1299 1265 hours-per-week <= 46.5 entropy = 0.995 samples = 24 value = [13, 11] 1264->1265 1294 workclass_Self-emp <= 0.5 entropy = 0.863 samples = 7 value = [2, 5] 1264->1294 1266 age <= 57.0 entropy = 0.971 samples = 5 value = [2, 3] 1265->1266 1271 age <= 57.5 entropy = 0.982 samples = 19 value = [11, 8] 1265->1271 1267 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 1266->1267 1270 entropy = 0.0 samples = 2 value = [0, 2] 1266->1270 1268 entropy = 1.0 samples = 2 value = [1, 1] 1267->1268 1269 entropy = 0.0 samples = 1 value = [1, 0] 1267->1269 1272 hours-per-week <= 49.0 entropy = 0.991 samples = 9 value = [4, 5] 1271->1272 1283 hours-per-week <= 49.0 entropy = 0.881 samples = 10 value = [7, 3] 1271->1283 1273 entropy = 0.0 samples = 1 value = [0, 1] 1272->1273 1274 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 8 value = [4, 4] 1272->1274 1275 age <= 56.5 entropy = 0.971 samples = 5 value = [3, 2] 1274->1275 1280 age <= 56.5 entropy = 0.918 samples = 3 value = [1, 2] 1274->1280 1276 age <= 55.5 entropy = 0.811 samples = 4 value = [3, 1] 1275->1276 1279 entropy = 0.0 samples = 1 value = [0, 1] 1275->1279 1277 entropy = 1.0 samples = 2 value = [1, 1] 1276->1277 1278 entropy = 0.0 samples = 2 value = [2, 0] 1276->1278 1281 entropy = 0.0 samples = 2 value = [0, 2] 1280->1281 1282 entropy = 0.0 samples = 1 value = [1, 0] 1280->1282 1284 entropy = 0.0 samples = 3 value = [3, 0] 1283->1284 1285 workclass_Self-emp <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 1283->1285 1286 age <= 59.5 entropy = 0.918 samples = 3 value = [1, 2] 1285->1286 1289 age <= 59.0 entropy = 0.811 samples = 4 value = [3, 1] 1285->1289 1287 entropy = 0.0 samples = 1 value = [0, 1] 1286->1287 1288 entropy = 1.0 samples = 2 value = [1, 1] 1286->1288 1290 entropy = 0.0 samples = 2 value = [2, 0] 1289->1290 1291 age <= 60.5 entropy = 1.0 samples = 2 value = [1, 1] 1289->1291 1292 entropy = 0.0 samples = 1 value = [0, 1] 1291->1292 1293 entropy = 0.0 samples = 1 value = [1, 0] 1291->1293 1295 age <= 57.0 entropy = 0.918 samples = 3 value = [2, 1] 1294->1295 1298 entropy = 0.0 samples = 4 value = [0, 4] 1294->1298 1296 entropy = 0.0 samples = 1 value = [0, 1] 1295->1296 1297 entropy = 0.0 samples = 2 value = [2, 0] 1295->1297 1300 workclass_Private <= 0.5 entropy = 0.722 samples = 10 value = [8, 2] 1299->1300 1309 entropy = 0.0 samples = 2 value = [0, 2] 1299->1309 1301 age <= 55.5 entropy = 0.918 samples = 6 value = [4, 2] 1300->1301 1308 entropy = 0.0 samples = 4 value = [4, 0] 1300->1308 1302 entropy = 0.0 samples = 1 value = [1, 0] 1301->1302 1303 age <= 57.0 entropy = 0.971 samples = 5 value = [3, 2] 1301->1303 1304 entropy = 0.0 samples = 1 value = [0, 1] 1303->1304 1305 age <= 58.5 entropy = 0.811 samples = 4 value = [3, 1] 1303->1305 1306 entropy = 0.918 samples = 3 value = [2, 1] 1305->1306 1307 entropy = 0.0 samples = 1 value = [1, 0] 1305->1307 1313 age <= 39.5 entropy = 0.938 samples = 31 value = [20, 11] 1312->1313 1344 entropy = 0.0 samples = 9 value = [9, 0] 1312->1344 1314 hours-per-week <= 91.5 entropy = 0.544 samples = 8 value = [7, 1] 1313->1314 1319 race_Asian <= 0.5 entropy = 0.988 samples = 23 value = [13, 10] 1313->1319 1315 entropy = 0.0 samples = 6 value = [6, 0] 1314->1315 1316 age <= 37.5 entropy = 1.0 samples = 2 value = [1, 1] 1314->1316 1317 entropy = 0.0 samples = 1 value = [0, 1] 1316->1317 1318 entropy = 0.0 samples = 1 value = [1, 0] 1316->1318 1320 age <= 43.5 entropy = 0.998 samples = 21 value = [11, 10] 1319->1320 1343 entropy = 0.0 samples = 2 value = [2, 0] 1319->1343 1321 entropy = 0.0 samples = 2 value = [0, 2] 1320->1321 1322 race_Black <= 0.5 entropy = 0.982 samples = 19 value = [11, 8] 1320->1322 1323 workclass_Public <= 0.5 entropy = 0.964 samples = 18 value = [11, 7] 1322->1323 1342 entropy = 0.0 samples = 1 value = [0, 1] 1322->1342 1324 hours-per-week <= 86.5 entropy = 0.997 samples = 15 value = [8, 7] 1323->1324 1341 entropy = 0.0 samples = 3 value = [3, 0] 1323->1341 1325 hours-per-week <= 72.5 entropy = 0.994 samples = 11 value = [5, 6] 1324->1325 1338 age <= 50.0 entropy = 0.811 samples = 4 value = [3, 1] 1324->1338 1326 workclass_Private <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] 1325->1326 1337 entropy = 0.0 samples = 3 value = [0, 3] 1325->1337 1327 entropy = 0.0 samples = 2 value = [2, 0] 1326->1327 1328 age <= 47.0 entropy = 1.0 samples = 6 value = [3, 3] 1326->1328 1329 age <= 45.0 entropy = 0.971 samples = 5 value = [2, 3] 1328->1329 1336 entropy = 0.0 samples = 1 value = [1, 0] 1328->1336 1330 hours-per-week <= 67.5 entropy = 0.918 samples = 3 value = [1, 2] 1329->1330 1333 hours-per-week <= 67.5 entropy = 1.0 samples = 2 value = [1, 1] 1329->1333 1331 entropy = 0.0 samples = 2 value = [0, 2] 1330->1331 1332 entropy = 0.0 samples = 1 value = [1, 0] 1330->1332 1334 entropy = 0.0 samples = 1 value = [1, 0] 1333->1334 1335 entropy = 0.0 samples = 1 value = [0, 1] 1333->1335 1339 entropy = 0.0 samples = 3 value = [3, 0] 1338->1339 1340 entropy = 0.0 samples = 1 value = [0, 1] 1338->1340 1347 entropy = 0.0 samples = 11 value = [11, 0] 1346->1347 1348 entropy = 0.0 samples = 1 value = [0, 1] 1346->1348 1350 age <= 47.5 entropy = 0.996 samples = 375 value = [202, 173] 1349->1350 1629 workclass_Private <= 0.5 entropy = 0.994 samples = 267 value = [121, 146] 1349->1629 1351 hours-per-week <= 39.0 entropy = 0.983 samples = 231 value = [133, 98] 1350->1351 1516 race_Asian <= 0.5 entropy = 0.999 samples = 144 value = [69, 75] 1350->1516 1352 workclass_Private <= 0.5 entropy = 0.779 samples = 13 value = [3, 10] 1351->1352 1363 education <= 10.5 entropy = 0.973 samples = 218 value = [130, 88] 1351->1363 1353 entropy = 0.0 samples = 5 value = [0, 5] 1352->1353 1354 age <= 37.5 entropy = 0.954 samples = 8 value = [3, 5] 1352->1354 1355 entropy = 0.0 samples = 1 value = [1, 0] 1354->1355 1356 age <= 46.0 entropy = 0.863 samples = 7 value = [2, 5] 1354->1356 1357 education <= 10.5 entropy = 0.65 samples = 6 value = [1, 5] 1356->1357 1362 entropy = 0.0 samples = 1 value = [1, 0] 1356->1362 1358 entropy = 0.0 samples = 4 value = [0, 4] 1357->1358 1359 race_Asian <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 1357->1359 1360 entropy = 0.0 samples = 1 value = [1, 0] 1359->1360 1361 entropy = 0.0 samples = 1 value = [0, 1] 1359->1361 1364 age <= 40.5 entropy = 0.942 samples = 170 value = [109, 61] 1363->1364 1477 sex_Male <= 0.5 entropy = 0.989 samples = 48 value = [21, 27] 1363->1477 1365 hours-per-week <= 41.0 entropy = 0.791 samples = 59 value = [45, 14] 1364->1365 1402 hours-per-week <= 42.5 entropy = 0.983 samples = 111 value = [64, 47] 1364->1402 1366 age <= 38.5 entropy = 0.737 samples = 53 value = [42, 11] 1365->1366 1399 hours-per-week <= 42.5 entropy = 1.0 samples = 6 value = [3, 3] 1365->1399 1367 race_Amer-Indian <= 0.5 entropy = 0.821 samples = 39 value = [29, 10] 1366->1367 1396 sex_Female <= 0.5 entropy = 0.371 samples = 14 value = [13, 1] 1366->1396 1368 race_Asian <= 0.5 entropy = 0.831 samples = 38 value = [28, 10] 1367->1368 1395 entropy = 0.0 samples = 1 value = [1, 0] 1367->1395 1369 workclass_Self-emp <= 0.5 entropy = 0.842 samples = 37 value = [27, 10] 1368->1369 1394 entropy = 0.0 samples = 1 value = [1, 0] 1368->1394 1370 race_Black <= 0.5 entropy = 0.822 samples = 35 value = [26, 9] 1369->1370 1391 age <= 37.5 entropy = 1.0 samples = 2 value = [1, 1] 1369->1391 1371 sex_Female <= 0.5 entropy = 0.784 samples = 30 value = [23, 7] 1370->1371 1386 age <= 37.0 entropy = 0.971 samples = 5 value = [3, 2] 1370->1386 1372 age <= 36.5 entropy = 0.75 samples = 28 value = [22, 6] 1371->1372 1383 workclass_Private <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 1371->1383 1373 workclass_Private <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 1372->1373 1376 workclass_Private <= 0.5 entropy = 0.755 samples = 23 value = [18, 5] 1372->1376 1374 entropy = 0.0 samples = 1 value = [0, 1] 1373->1374 1375 entropy = 0.0 samples = 4 value = [4, 0] 1373->1375 1377 age <= 37.5 entropy = 0.544 samples = 8 value = [7, 1] 1376->1377 1380 age <= 37.5 entropy = 0.837 samples = 15 value = [11, 4] 1376->1380 1378 entropy = 0.722 samples = 5 value = [4, 1] 1377->1378 1379 entropy = 0.0 samples = 3 value = [3, 0] 1377->1379 1381 entropy = 0.811 samples = 4 value = [3, 1] 1380->1381 1382 entropy = 0.845 samples = 11 value = [8, 3] 1380->1382 1384 entropy = 0.0 samples = 1 value = [1, 0] 1383->1384 1385 entropy = 0.0 samples = 1 value = [0, 1] 1383->1385 1387 entropy = 0.0 samples = 1 value = [0, 1] 1386->1387 1388 sex_Male <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 1386->1388 1389 entropy = 0.0 samples = 2 value = [2, 0] 1388->1389 1390 entropy = 1.0 samples = 2 value = [1, 1] 1388->1390 1392 entropy = 0.0 samples = 1 value = [1, 0] 1391->1392 1393 entropy = 0.0 samples = 1 value = [0, 1] 1391->1393 1397 entropy = 0.0 samples = 12 value = [12, 0] 1396->1397 1398 entropy = 1.0 samples = 2 value = [1, 1] 1396->1398 1400 entropy = 0.0 samples = 3 value = [0, 3] 1399->1400 1401 entropy = 0.0 samples = 3 value = [3, 0] 1399->1401 1403 workclass_Public <= 0.5 entropy = 0.981 samples = 110 value = [64, 46] 1402->1403 1476 entropy = 0.0 samples = 1 value = [0, 1] 1402->1476 1404 age <= 44.5 entropy = 0.964 samples = 85 value = [52, 33] 1403->1404 1455 age <= 44.5 entropy = 0.999 samples = 25 value = [12, 13] 1403->1455 1405 race_Asian <= 0.5 entropy = 0.993 samples = 51 value = [28, 23] 1404->1405 1436 race_Black <= 0.5 entropy = 0.874 samples = 34 value = [24, 10] 1404->1436 1406 hours-per-week <= 41.0 entropy = 0.99 samples = 50 value = [28, 22] 1405->1406 1435 entropy = 0.0 samples = 1 value = [0, 1] 1405->1435 1407 age <= 42.5 entropy = 0.992 samples = 49 value = [27, 22] 1406->1407 1434 entropy = 0.0 samples = 1 value = [1, 0] 1406->1434 1408 sex_Female <= 0.5 entropy = 0.998 samples = 21 value = [10, 11] 1407->1408 1421 sex_Female <= 0.5 entropy = 0.967 samples = 28 value = [17, 11] 1407->1421 1409 workclass_Private <= 0.5 entropy = 1.0 samples = 20 value = [10, 10] 1408->1409 1420 entropy = 0.0 samples = 1 value = [0, 1] 1408->1420 1410 age <= 41.5 entropy = 0.918 samples = 3 value = [1, 2] 1409->1410 1413 race_White <= 0.5 entropy = 0.998 samples = 17 value = [9, 8] 1409->1413 1411 entropy = 0.0 samples = 1 value = [0, 1] 1410->1411 1412 entropy = 1.0 samples = 2 value = [1, 1] 1410->1412 1414 age <= 41.5 entropy = 0.918 samples = 3 value = [1, 2] 1413->1414 1417 age <= 41.5 entropy = 0.985 samples = 14 value = [8, 6] 1413->1417 1415 entropy = 0.0 samples = 1 value = [1, 0] 1414->1415 1416 entropy = 0.0 samples = 2 value = [0, 2] 1414->1416 1418 entropy = 1.0 samples = 6 value = [3, 3] 1417->1418 1419 entropy = 0.954 samples = 8 value = [5, 3] 1417->1419 1422 age <= 43.5 entropy = 0.99 samples = 25 value = [14, 11] 1421->1422 1433 entropy = 0.0 samples = 3 value = [3, 0] 1421->1433 1423 workclass_Private <= 0.5 entropy = 1.0 samples = 14 value = [7, 7] 1422->1423 1428 workclass_Self-emp <= 0.5 entropy = 0.946 samples = 11 value = [7, 4] 1422->1428 1424 entropy = 0.0 samples = 1 value = [1, 0] 1423->1424 1425 race_White <= 0.5 entropy = 0.996 samples = 13 value = [6, 7] 1423->1425 1426 entropy = 0.0 samples = 1 value = [0, 1] 1425->1426 1427 entropy = 1.0 samples = 12 value = [6, 6] 1425->1427 1429 race_Black <= 0.5 entropy = 0.881 samples = 10 value = [7, 3] 1428->1429 1432 entropy = 0.0 samples = 1 value = [0, 1] 1428->1432 1430 entropy = 0.918 samples = 9 value = [6, 3] 1429->1430 1431 entropy = 0.0 samples = 1 value = [1, 0] 1429->1431 1437 race_White <= 0.5 entropy = 0.784 samples = 30 value = [23, 7] 1436->1437 1452 age <= 46.5 entropy = 0.811 samples = 4 value = [1, 3] 1436->1452 1438 entropy = 0.0 samples = 4 value = [4, 0] 1437->1438 1439 age <= 46.5 entropy = 0.84 samples = 26 value = [19, 7] 1437->1439 1440 workclass_Private <= 0.5 entropy = 0.937 samples = 17 value = [11, 6] 1439->1440 1449 workclass_Private <= 0.5 entropy = 0.503 samples = 9 value = [8, 1] 1439->1449 1441 entropy = 1.0 samples = 2 value = [1, 1] 1440->1441 1442 sex_Female <= 0.5 entropy = 0.918 samples = 15 value = [10, 5] 1440->1442 1443 age <= 45.5 entropy = 0.89 samples = 13 value = [9, 4] 1442->1443 1446 age <= 45.5 entropy = 1.0 samples = 2 value = [1, 1] 1442->1446 1444 entropy = 0.971 samples = 5 value = [3, 2] 1443->1444 1445 entropy = 0.811 samples = 8 value = [6, 2] 1443->1445 1447 entropy = 0.0 samples = 1 value = [1, 0] 1446->1447 1448 entropy = 0.0 samples = 1 value = [0, 1] 1446->1448 1450 entropy = 0.0 samples = 2 value = [2, 0] 1449->1450 1451 entropy = 0.592 samples = 7 value = [6, 1] 1449->1451 1453 entropy = 1.0 samples = 2 value = [1, 1] 1452->1453 1454 entropy = 0.0 samples = 2 value = [0, 2] 1452->1454 1456 age <= 43.5 entropy = 0.989 samples = 16 value = [9, 7] 1455->1456 1469 age <= 46.5 entropy = 0.918 samples = 9 value = [3, 6] 1455->1469 1457 race_Black <= 0.5 entropy = 1.0 samples = 14 value = [7, 7] 1456->1457 1468 entropy = 0.0 samples = 2 value = [2, 0] 1456->1468 1458 age <= 41.5 entropy = 0.996 samples = 13 value = [6, 7] 1457->1458 1467 entropy = 0.0 samples = 1 value = [1, 0] 1457->1467 1459 entropy = 0.971 samples = 5 value = [3, 2] 1458->1459 1460 sex_Male <= 0.5 entropy = 0.954 samples = 8 value = [3, 5] 1458->1460 1461 age <= 42.5 entropy = 1.0 samples = 2 value = [1, 1] 1460->1461 1464 age <= 42.5 entropy = 0.918 samples = 6 value = [2, 4] 1460->1464 1462 entropy = 0.0 samples = 1 value = [1, 0] 1461->1462 1463 entropy = 0.0 samples = 1 value = [0, 1] 1461->1463 1465 entropy = 0.0 samples = 2 value = [0, 2] 1464->1465 1466 entropy = 1.0 samples = 4 value = [2, 2] 1464->1466 1470 age <= 45.5 entropy = 0.722 samples = 5 value = [1, 4] 1469->1470 1473 race_White <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 1469->1473 1471 entropy = 0.811 samples = 4 value = [1, 3] 1470->1471 1472 entropy = 0.0 samples = 1 value = [0, 1] 1470->1472 1474 entropy = 0.0 samples = 1 value = [0, 1] 1473->1474 1475 entropy = 0.918 samples = 3 value = [2, 1] 1473->1475 1478 entropy = 0.0 samples = 4 value = [0, 4] 1477->1478 1479 race_Asian <= 0.5 entropy = 0.999 samples = 44 value = [21, 23] 1477->1479 1480 age <= 39.0 entropy = 1.0 samples = 42 value = [21, 21] 1479->1480 1515 entropy = 0.0 samples = 2 value = [0, 2] 1479->1515 1481 age <= 36.5 entropy = 0.845 samples = 11 value = [3, 8] 1480->1481 1486 workclass_Self-emp <= 0.5 entropy = 0.981 samples = 31 value = [18, 13] 1480->1486 1482 entropy = 0.0 samples = 1 value = [0, 1] 1481->1482 1483 age <= 37.5 entropy = 0.881 samples = 10 value = [3, 7] 1481->1483 1484 entropy = 0.811 samples = 4 value = [1, 3] 1483->1484 1485 entropy = 0.918 samples = 6 value = [2, 4] 1483->1485 1487 hours-per-week <= 41.0 entropy = 0.971 samples = 30 value = [18, 12] 1486->1487 1514 entropy = 0.0 samples = 1 value = [0, 1] 1486->1514 1488 age <= 46.5 entropy = 0.978 samples = 29 value = [17, 12] 1487->1488 1513 entropy = 0.0 samples = 1 value = [1, 0] 1487->1513 1489 age <= 45.5 entropy = 0.954 samples = 24 value = [15, 9] 1488->1489 1510 workclass_Public <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 1488->1510 1490 age <= 41.5 entropy = 0.976 samples = 22 value = [13, 9] 1489->1490 1509 entropy = 0.0 samples = 2 value = [2, 0] 1489->1509 1491 workclass_Public <= 0.5 entropy = 0.881 samples = 10 value = [7, 3] 1490->1491 1498 age <= 43.0 entropy = 1.0 samples = 12 value = [6, 6] 1490->1498 1492 race_White <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] 1491->1492 1497 entropy = 0.0 samples = 2 value = [2, 0] 1491->1497 1493 entropy = 0.0 samples = 1 value = [1, 0] 1492->1493 1494 age <= 40.5 entropy = 0.985 samples = 7 value = [4, 3] 1492->1494 1495 entropy = 0.971 samples = 5 value = [3, 2] 1494->1495 1496 entropy = 1.0 samples = 2 value = [1, 1] 1494->1496 1499 entropy = 0.0 samples = 2 value = [0, 2] 1498->1499 1500 workclass_Private <= 0.5 entropy = 0.971 samples = 10 value = [6, 4] 1498->1500 1501 age <= 44.5 entropy = 1.0 samples = 2 value = [1, 1] 1500->1501 1504 age <= 44.5 entropy = 0.954 samples = 8 value = [5, 3] 1500->1504 1502 entropy = 0.0 samples = 1 value = [0, 1] 1501->1502 1503 entropy = 0.0 samples = 1 value = [1, 0] 1501->1503 1505 race_Black <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 1504->1505 1508 entropy = 1.0 samples = 2 value = [1, 1] 1504->1508 1506 entropy = 0.918 samples = 3 value = [2, 1] 1505->1506 1507 entropy = 0.918 samples = 3 value = [2, 1] 1505->1507 1511 entropy = 1.0 samples = 4 value = [2, 2] 1510->1511 1512 entropy = 0.0 samples = 1 value = [0, 1] 1510->1512 1517 hours-per-week <= 37.5 entropy = 1.0 samples = 139 value = [69, 70] 1516->1517 1628 entropy = 0.0 samples = 5 value = [0, 5] 1516->1628 1518 age <= 50.5 entropy = 0.722 samples = 10 value = [8, 2] 1517->1518 1527 age <= 59.5 entropy = 0.998 samples = 129 value = [61, 68] 1517->1527 1519 sex_Male <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 1518->1519 1526 entropy = 0.0 samples = 6 value = [6, 0] 1518->1526 1520 entropy = 0.0 samples = 1 value = [1, 0] 1519->1520 1521 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 1519->1521 1522 age <= 49.0 entropy = 1.0 samples = 2 value = [1, 1] 1521->1522 1525 entropy = 0.0 samples = 1 value = [0, 1] 1521->1525 1523 entropy = 0.0 samples = 1 value = [0, 1] 1522->1523 1524 entropy = 0.0 samples = 1 value = [1, 0] 1522->1524 1528 sex_Female <= 0.5 entropy = 0.994 samples = 117 value = [53, 64] 1527->1528 1617 hours-per-week <= 39.0 entropy = 0.918 samples = 12 value = [8, 4] 1527->1617 1529 race_White <= 0.5 entropy = 0.979 samples = 94 value = [39, 55] 1528->1529 1592 race_White <= 0.5 entropy = 0.966 samples = 23 value = [14, 9] 1528->1592 1530 entropy = 0.0 samples = 5 value = [0, 5] 1529->1530 1531 age <= 54.5 entropy = 0.989 samples = 89 value = [39, 50] 1529->1531 1532 workclass_Self-emp <= 0.5 entropy = 0.999 samples = 66 value = [32, 34] 1531->1532 1569 workclass_Self-emp <= 0.5 entropy = 0.887 samples = 23 value = [7, 16] 1531->1569 1533 hours-per-week <= 41.0 entropy = 0.993 samples = 62 value = [28, 34] 1532->1533 1568 entropy = 0.0 samples = 4 value = [4, 0] 1532->1568 1534 age <= 49.5 entropy = 0.987 samples = 60 value = [26, 34] 1533->1534 1567 entropy = 0.0 samples = 2 value = [2, 0] 1533->1567 1535 education <= 10.5 entropy = 0.787 samples = 17 value = [4, 13] 1534->1535 1544 education <= 10.5 entropy = 1.0 samples = 43 value = [22, 21] 1534->1544 1536 age <= 48.5 entropy = 0.811 samples = 16 value = [4, 12] 1535->1536 1543 entropy = 0.0 samples = 1 value = [0, 1] 1535->1543 1537 workclass_Public <= 0.5 entropy = 0.65 samples = 6 value = [1, 5] 1536->1537 1540 workclass_Private <= 0.5 entropy = 0.881 samples = 10 value = [3, 7] 1536->1540 1538 entropy = 0.722 samples = 5 value = [1, 4] 1537->1538 1539 entropy = 0.0 samples = 1 value = [0, 1] 1537->1539 1541 entropy = 0.918 samples = 6 value = [2, 4] 1540->1541 1542 entropy = 0.811 samples = 4 value = [1, 3] 1540->1542 1545 age <= 52.5 entropy = 0.997 samples = 32 value = [15, 17] 1544->1545 1560 age <= 51.5 entropy = 0.946 samples = 11 value = [7, 4] 1544->1560 1546 age <= 50.5 entropy = 0.985 samples = 21 value = [9, 12] 1545->1546 1555 age <= 53.5 entropy = 0.994 samples = 11 value = [6, 5] 1545->1555 1547 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 1546->1547 1550 workclass_Public <= 0.5 entropy = 0.991 samples = 18 value = [8, 10] 1546->1550 1548 entropy = 1.0 samples = 2 value = [1, 1] 1547->1548 1549 entropy = 0.0 samples = 1 value = [0, 1] 1547->1549 1551 age <= 51.5 entropy = 0.977 samples = 17 value = [7, 10] 1550->1551 1554 entropy = 0.0 samples = 1 value = [1, 0] 1550->1554 1552 entropy = 0.971 samples = 10 value = [4, 6] 1551->1552 1553 entropy = 0.985 samples = 7 value = [3, 4] 1551->1553 1556 entropy = 0.971 samples = 5 value = [3, 2] 1555->1556 1557 workclass_Private <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 1555->1557 1558 entropy = 1.0 samples = 2 value = [1, 1] 1557->1558 1559 entropy = 1.0 samples = 4 value = [2, 2] 1557->1559 1561 age <= 50.5 entropy = 1.0 samples = 6 value = [3, 3] 1560->1561 1564 age <= 52.5 entropy = 0.722 samples = 5 value = [4, 1] 1560->1564 1562 entropy = 0.0 samples = 1 value = [1, 0] 1561->1562 1563 entropy = 0.971 samples = 5 value = [2, 3] 1561->1563 1565 entropy = 0.0 samples = 3 value = [3, 0] 1564->1565 1566 entropy = 1.0 samples = 2 value = [1, 1] 1564->1566 1570 hours-per-week <= 41.5 entropy = 0.918 samples = 21 value = [7, 14] 1569->1570 1591 entropy = 0.0 samples = 2 value = [0, 2] 1569->1591 1571 age <= 56.5 entropy = 0.934 samples = 20 value = [7, 13] 1570->1571 1590 entropy = 0.0 samples = 1 value = [0, 1] 1570->1590 1572 age <= 55.5 entropy = 0.722 samples = 5 value = [1, 4] 1571->1572 1575 workclass_Private <= 0.5 entropy = 0.971 samples = 15 value = [6, 9] 1571->1575 1573 entropy = 0.918 samples = 3 value = [1, 2] 1572->1573 1574 entropy = 0.0 samples = 2 value = [0, 2] 1572->1574 1576 age <= 58.5 entropy = 1.0 samples = 2 value = [1, 1] 1575->1576 1579 age <= 58.5 entropy = 0.961 samples = 13 value = [5, 8] 1575->1579 1577 entropy = 0.0 samples = 1 value = [0, 1] 1576->1577 1578 entropy = 0.0 samples = 1 value = [1, 0] 1576->1578 1580 education <= 10.5 entropy = 0.991 samples = 9 value = [4, 5] 1579->1580 1587 education <= 10.5 entropy = 0.811 samples = 4 value = [1, 3] 1579->1587 1581 age <= 57.5 entropy = 0.971 samples = 5 value = [2, 3] 1580->1581 1584 age <= 57.5 entropy = 1.0 samples = 4 value = [2, 2] 1580->1584 1582 entropy = 0.811 samples = 4 value = [1, 3] 1581->1582 1583 entropy = 0.0 samples = 1 value = [1, 0] 1581->1583 1585 entropy = 0.0 samples = 1 value = [1, 0] 1584->1585 1586 entropy = 0.918 samples = 3 value = [1, 2] 1584->1586 1588 entropy = 0.918 samples = 3 value = [1, 2] 1587->1588 1589 entropy = 0.0 samples = 1 value = [0, 1] 1587->1589 1593 entropy = 0.0 samples = 4 value = [4, 0] 1592->1593 1594 education <= 10.5 entropy = 0.998 samples = 19 value = [10, 9] 1592->1594 1595 age <= 52.0 entropy = 0.998 samples = 17 value = [8, 9] 1594->1595 1616 entropy = 0.0 samples = 2 value = [2, 0] 1594->1616 1596 workclass_Public <= 0.5 entropy = 0.918 samples = 9 value = [3, 6] 1595->1596 1607 workclass_Public <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] 1595->1607 1597 age <= 48.5 entropy = 0.954 samples = 8 value = [3, 5] 1596->1597 1606 entropy = 0.0 samples = 1 value = [0, 1] 1596->1606 1598 entropy = 0.0 samples = 1 value = [1, 0] 1597->1598 1599 age <= 49.5 entropy = 0.863 samples = 7 value = [2, 5] 1597->1599 1600 entropy = 0.0 samples = 2 value = [0, 2] 1599->1600 1601 hours-per-week <= 39.0 entropy = 0.971 samples = 5 value = [2, 3] 1599->1601 1602 entropy = 0.0 samples = 1 value = [0, 1] 1601->1602 1603 age <= 50.5 entropy = 1.0 samples = 4 value = [2, 2] 1601->1603 1604 entropy = 0.0 samples = 1 value = [1, 0] 1603->1604 1605 entropy = 0.918 samples = 3 value = [1, 2] 1603->1605 1608 age <= 56.5 entropy = 1.0 samples = 6 value = [3, 3] 1607->1608 1615 entropy = 0.0 samples = 2 value = [2, 0] 1607->1615 1609 age <= 53.5 entropy = 0.971 samples = 5 value = [3, 2] 1608->1609 1614 entropy = 0.0 samples = 1 value = [0, 1] 1608->1614 1610 entropy = 1.0 samples = 2 value = [1, 1] 1609->1610 1611 age <= 54.5 entropy = 0.918 samples = 3 value = [2, 1] 1609->1611 1612 entropy = 0.0 samples = 1 value = [1, 0] 1611->1612 1613 entropy = 1.0 samples = 2 value = [1, 1] 1611->1613 1618 entropy = 0.0 samples = 1 value = [0, 1] 1617->1618 1619 age <= 60.5 entropy = 0.845 samples = 11 value = [8, 3] 1617->1619 1620 entropy = 0.0 samples = 2 value = [2, 0] 1619->1620 1621 education <= 10.5 entropy = 0.918 samples = 9 value = [6, 3] 1619->1621 1622 age <= 61.5 entropy = 0.811 samples = 8 value = [6, 2] 1621->1622 1627 entropy = 0.0 samples = 1 value = [0, 1] 1621->1627 1623 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 1622->1623 1626 entropy = 0.0 samples = 2 value = [2, 0] 1622->1626 1624 entropy = 0.971 samples = 5 value = [3, 2] 1623->1624 1625 entropy = 0.0 samples = 1 value = [1, 0] 1623->1625 1630 sex_Male <= 0.5 entropy = 0.997 samples = 111 value = [59, 52] 1629->1630 1725 age <= 60.5 entropy = 0.969 samples = 156 value = [62, 94] 1629->1725 1631 entropy = 0.0 samples = 4 value = [4, 0] 1630->1631 1632 age <= 57.5 entropy = 0.999 samples = 107 value = [55, 52] 1630->1632 1633 hours-per-week <= 58.5 entropy = 0.995 samples = 100 value = [54, 46] 1632->1633 1722 race_Asian <= 0.5 entropy = 0.592 samples = 7 value = [1, 6] 1632->1722 1634 hours-per-week <= 53.0 entropy = 0.992 samples = 56 value = [25, 31] 1633->1634 1677 hours-per-week <= 94.5 entropy = 0.926 samples = 44 value = [29, 15] 1633->1677 1635 age <= 49.5 entropy = 0.999 samples = 46 value = [24, 22] 1634->1635 1674 age <= 55.5 entropy = 0.469 samples = 10 value = [1, 9] 1634->1674 1636 age <= 48.5 entropy = 0.981 samples = 31 value = [13, 18] 1635->1636 1659 age <= 53.5 entropy = 0.837 samples = 15 value = [11, 4] 1635->1659 1637 age <= 43.5 entropy = 0.999 samples = 27 value = [13, 14] 1636->1637 1658 entropy = 0.0 samples = 4 value = [0, 4] 1636->1658 1638 hours-per-week <= 49.0 entropy = 0.977 samples = 17 value = [10, 7] 1637->1638 1649 hours-per-week <= 46.5 entropy = 0.881 samples = 10 value = [3, 7] 1637->1649 1639 hours-per-week <= 44.5 entropy = 0.65 samples = 6 value = [1, 5] 1638->1639 1642 age <= 37.5 entropy = 0.684 samples = 11 value = [9, 2] 1638->1642 1640 entropy = 0.0 samples = 1 value = [1, 0] 1639->1640 1641 entropy = 0.0 samples = 5 value = [0, 5] 1639->1641 1643 hours-per-week <= 51.0 entropy = 1.0 samples = 2 value = [1, 1] 1642->1643 1646 age <= 42.5 entropy = 0.503 samples = 9 value = [8, 1] 1642->1646 1644 entropy = 0.0 samples = 1 value = [0, 1] 1643->1644 1645 entropy = 0.0 samples = 1 value = [1, 0] 1643->1645 1647 entropy = 0.0 samples = 5 value = [5, 0] 1646->1647 1648 entropy = 0.811 samples = 4 value = [3, 1] 1646->1648 1650 entropy = 0.0 samples = 2 value = [2, 0] 1649->1650 1651 age <= 46.0 entropy = 0.544 samples = 8 value = [1, 7] 1649->1651 1652 entropy = 0.0 samples = 5 value = [0, 5] 1651->1652 1653 hours-per-week <= 49.0 entropy = 0.918 samples = 3 value = [1, 2] 1651->1653 1654 entropy = 0.0 samples = 1 value = [0, 1] 1653->1654 1655 race_Black <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 1653->1655 1656 entropy = 0.0 samples = 1 value = [1, 0] 1655->1656 1657 entropy = 0.0 samples = 1 value = [0, 1] 1655->1657 1660 age <= 52.5 entropy = 0.592 samples = 7 value = [6, 1] 1659->1660 1665 hours-per-week <= 49.0 entropy = 0.954 samples = 8 value = [5, 3] 1659->1665 1661 hours-per-week <= 49.0 entropy = 0.811 samples = 4 value = [3, 1] 1660->1661 1664 entropy = 0.0 samples = 3 value = [3, 0] 1660->1664 1662 entropy = 0.0 samples = 2 value = [2, 0] 1661->1662 1663 entropy = 1.0 samples = 2 value = [1, 1] 1661->1663 1666 age <= 55.0 entropy = 0.918 samples = 3 value = [1, 2] 1665->1666 1669 age <= 55.0 entropy = 0.722 samples = 5 value = [4, 1] 1665->1669 1667 entropy = 0.0 samples = 2 value = [0, 2] 1666->1667 1668 entropy = 0.0 samples = 1 value = [1, 0] 1666->1668 1670 entropy = 0.0 samples = 2 value = [2, 0] 1669->1670 1671 age <= 56.5 entropy = 0.918 samples = 3 value = [2, 1] 1669->1671 1672 entropy = 1.0 samples = 2 value = [1, 1] 1671->1672 1673 entropy = 0.0 samples = 1 value = [1, 0] 1671->1673 1675 entropy = 0.0 samples = 9 value = [0, 9] 1674->1675 1676 entropy = 0.0 samples = 1 value = [1, 0] 1674->1676 1678 age <= 43.5 entropy = 0.893 samples = 42 value = [29, 13] 1677->1678 1721 entropy = 0.0 samples = 2 value = [0, 2] 1677->1721 1679 age <= 41.5 entropy = 0.702 samples = 21 value = [17, 4] 1678->1679 1698 age <= 44.5 entropy = 0.985 samples = 21 value = [12, 9] 1678->1698 1680 race_Asian <= 0.5 entropy = 0.811 samples = 16 value = [12, 4] 1679->1680 1697 entropy = 0.0 samples = 5 value = [5, 0] 1679->1697 1681 hours-per-week <= 71.0 entropy = 0.722 samples = 15 value = [12, 3] 1680->1681 1696 entropy = 0.0 samples = 1 value = [0, 1] 1680->1696 1682 workclass_Public <= 0.5 entropy = 0.811 samples = 12 value = [9, 3] 1681->1682 1695 entropy = 0.0 samples = 3 value = [3, 0] 1681->1695 1683 hours-per-week <= 67.5 entropy = 0.722 samples = 10 value = [8, 2] 1682->1683 1692 age <= 38.0 entropy = 1.0 samples = 2 value = [1, 1] 1682->1692 1684 age <= 36.5 entropy = 0.544 samples = 8 value = [7, 1] 1683->1684 1689 education <= 10.5 entropy = 1.0 samples = 2 value = [1, 1] 1683->1689 1685 education <= 10.5 entropy = 1.0 samples = 2 value = [1, 1] 1684->1685 1688 entropy = 0.0 samples = 6 value = [6, 0] 1684->1688 1686 entropy = 0.0 samples = 1 value = [0, 1] 1685->1686 1687 entropy = 0.0 samples = 1 value = [1, 0] 1685->1687 1690 entropy = 0.0 samples = 1 value = [1, 0] 1689->1690 1691 entropy = 0.0 samples = 1 value = [0, 1] 1689->1691 1693 entropy = 0.0 samples = 1 value = [1, 0] 1692->1693 1694 entropy = 0.0 samples = 1 value = [0, 1] 1692->1694 1699 entropy = 0.0 samples = 2 value = [0, 2] 1698->1699 1700 age <= 56.5 entropy = 0.949 samples = 19 value = [12, 7] 1698->1700 1701 age <= 50.5 entropy = 0.918 samples = 18 value = [12, 6] 1700->1701 1720 entropy = 0.0 samples = 1 value = [0, 1] 1700->1720 1702 age <= 45.5 entropy = 0.985 samples = 14 value = [8, 6] 1701->1702 1719 entropy = 0.0 samples = 4 value = [4, 0] 1701->1719 1703 entropy = 0.0 samples = 2 value = [2, 0] 1702->1703 1704 education <= 10.5 entropy = 1.0 samples = 12 value = [6, 6] 1702->1704 1705 age <= 48.5 entropy = 0.954 samples = 8 value = [5, 3] 1704->1705 1716 age <= 47.5 entropy = 0.811 samples = 4 value = [1, 3] 1704->1716 1706 hours-per-week <= 65.0 entropy = 1.0 samples = 4 value = [2, 2] 1705->1706 1711 age <= 49.5 entropy = 0.811 samples = 4 value = [3, 1] 1705->1711 1707 age <= 47.0 entropy = 0.918 samples = 3 value = [2, 1] 1706->1707 1710 entropy = 0.0 samples = 1 value = [0, 1] 1706->1710 1708 entropy = 1.0 samples = 2 value = [1, 1] 1707->1708 1709 entropy = 0.0 samples = 1 value = [1, 0] 1707->1709 1712 entropy = 0.0 samples = 2 value = [2, 0] 1711->1712 1713 hours-per-week <= 65.0 entropy = 1.0 samples = 2 value = [1, 1] 1711->1713 1714 entropy = 0.0 samples = 1 value = [0, 1] 1713->1714 1715 entropy = 0.0 samples = 1 value = [1, 0] 1713->1715 1717 entropy = 1.0 samples = 2 value = [1, 1] 1716->1717 1718 entropy = 0.0 samples = 2 value = [0, 2] 1716->1718 1723 entropy = 0.0 samples = 6 value = [0, 6] 1722->1723 1724 entropy = 0.0 samples = 1 value = [1, 0] 1722->1724 1726 age <= 59.5 entropy = 0.974 samples = 153 value = [62, 91] 1725->1726 1873 entropy = 0.0 samples = 3 value = [0, 3] 1725->1873 1727 hours-per-week <= 59.0 entropy = 0.969 samples = 151 value = [60, 91] 1726->1727 1872 entropy = 0.0 samples = 2 value = [2, 0] 1726->1872 1728 hours-per-week <= 53.0 entropy = 0.986 samples = 128 value = [55, 73] 1727->1728 1855 hours-per-week <= 91.5 entropy = 0.755 samples = 23 value = [5, 18] 1727->1855 1729 hours-per-week <= 45.5 entropy = 0.953 samples = 110 value = [41, 69] 1728->1729 1836 sex_Female <= 0.5 entropy = 0.764 samples = 18 value = [14, 4] 1728->1836 1730 hours-per-week <= 44.5 entropy = 0.994 samples = 44 value = [20, 24] 1729->1730 1771 education <= 10.5 entropy = 0.902 samples = 66 value = [21, 45] 1729->1771 1731 age <= 39.5 entropy = 0.764 samples = 9 value = [2, 7] 1730->1731 1738 age <= 36.5 entropy = 0.999 samples = 35 value = [18, 17] 1730->1738 1732 age <= 36.5 entropy = 0.918 samples = 6 value = [2, 4] 1731->1732 1737 entropy = 0.0 samples = 3 value = [0, 3] 1731->1737 1733 entropy = 1.0 samples = 2 value = [1, 1] 1732->1733 1734 age <= 37.5 entropy = 0.811 samples = 4 value = [1, 3] 1732->1734 1735 entropy = 0.0 samples = 1 value = [0, 1] 1734->1735 1736 entropy = 0.918 samples = 3 value = [1, 2] 1734->1736 1739 entropy = 0.0 samples = 2 value = [2, 0] 1738->1739 1740 sex_Female <= 0.5 entropy = 0.999 samples = 33 value = [16, 17] 1738->1740 1741 age <= 54.5 entropy = 0.992 samples = 29 value = [13, 16] 1740->1741 1768 age <= 41.0 entropy = 0.811 samples = 4 value = [3, 1] 1740->1768 1742 age <= 50.5 entropy = 0.966 samples = 23 value = [9, 14] 1741->1742 1761 age <= 55.5 entropy = 0.918 samples = 6 value = [4, 2] 1741->1761 1743 age <= 46.5 entropy = 1.0 samples = 18 value = [9, 9] 1742->1743 1760 entropy = 0.0 samples = 5 value = [0, 5] 1742->1760 1744 age <= 45.0 entropy = 0.971 samples = 10 value = [4, 6] 1743->1744 1755 age <= 48.0 entropy = 0.954 samples = 8 value = [5, 3] 1743->1755 1745 age <= 43.0 entropy = 1.0 samples = 8 value = [4, 4] 1744->1745 1754 entropy = 0.0 samples = 2 value = [0, 2] 1744->1754 1746 age <= 39.5 entropy = 0.985 samples = 7 value = [3, 4] 1745->1746 1753 entropy = 0.0 samples = 1 value = [1, 0] 1745->1753 1747 education <= 10.5 entropy = 0.971 samples = 5 value = [3, 2] 1746->1747 1752 entropy = 0.0 samples = 2 value = [0, 2] 1746->1752 1748 age <= 38.0 entropy = 0.811 samples = 4 value = [3, 1] 1747->1748 1751 entropy = 0.0 samples = 1 value = [0, 1] 1747->1751 1749 entropy = 0.918 samples = 3 value = [2, 1] 1748->1749 1750 entropy = 0.0 samples = 1 value = [1, 0] 1748->1750 1756 entropy = 0.0 samples = 3 value = [3, 0] 1755->1756 1757 education <= 10.5 entropy = 0.971 samples = 5 value = [2, 3] 1755->1757 1758 entropy = 0.0 samples = 3 value = [0, 3] 1757->1758 1759 entropy = 0.0 samples = 2 value = [2, 0] 1757->1759 1762 entropy = 0.0 samples = 1 value = [1, 0] 1761->1762 1763 education <= 10.5 entropy = 0.971 samples = 5 value = [3, 2] 1761->1763 1764 age <= 57.5 entropy = 0.811 samples = 4 value = [3, 1] 1763->1764 1767 entropy = 0.0 samples = 1 value = [0, 1] 1763->1767 1765 entropy = 0.0 samples = 2 value = [2, 0] 1764->1765 1766 entropy = 1.0 samples = 2 value = [1, 1] 1764->1766 1769 entropy = 0.0 samples = 1 value = [0, 1] 1768->1769 1770 entropy = 0.0 samples = 3 value = [3, 0] 1768->1770 1772 sex_Male <= 0.5 entropy = 0.94 samples = 56 value = [20, 36] 1771->1772 1833 age <= 52.5 entropy = 0.469 samples = 10 value = [1, 9] 1771->1833 1773 entropy = 0.0 samples = 2 value = [0, 2] 1772->1773 1774 age <= 45.0 entropy = 0.951 samples = 54 value = [20, 34] 1772->1774 1775 age <= 43.5 entropy = 0.985 samples = 35 value = [15, 20] 1774->1775 1816 hours-per-week <= 49.0 entropy = 0.831 samples = 19 value = [5, 14] 1774->1816 1776 hours-per-week <= 47.0 entropy = 0.977 samples = 34 value = [14, 20] 1775->1776 1815 entropy = 0.0 samples = 1 value = [1, 0] 1775->1815 1777 entropy = 0.0 samples = 1 value = [0, 1] 1776->1777 1778 race_Amer-Indian <= 0.5 entropy = 0.983 samples = 33 value = [14, 19] 1776->1778 1779 race_Black <= 0.5 entropy = 0.989 samples = 32 value = [14, 18] 1778->1779 1814 entropy = 0.0 samples = 1 value = [0, 1] 1778->1814 1780 age <= 41.5 entropy = 0.978 samples = 29 value = [12, 17] 1779->1780 1811 hours-per-week <= 51.0 entropy = 0.918 samples = 3 value = [2, 1] 1779->1811 1781 hours-per-week <= 51.0 entropy = 0.946 samples = 22 value = [8, 14] 1780->1781 1804 hours-per-week <= 51.0 entropy = 0.985 samples = 7 value = [4, 3] 1780->1804 1782 age <= 36.5 entropy = 0.852 samples = 18 value = [5, 13] 1781->1782 1799 age <= 38.5 entropy = 0.811 samples = 4 value = [3, 1] 1781->1799 1783 entropy = 0.0 samples = 2 value = [0, 2] 1782->1783 1784 age <= 38.5 entropy = 0.896 samples = 16 value = [5, 11] 1782->1784 1785 age <= 37.5 entropy = 0.722 samples = 5 value = [1, 4] 1784->1785 1790 age <= 39.5 entropy = 0.946 samples = 11 value = [4, 7] 1784->1790 1786 hours-per-week <= 49.0 entropy = 0.918 samples = 3 value = [1, 2] 1785->1786 1789 entropy = 0.0 samples = 2 value = [0, 2] 1785->1789 1787 entropy = 0.0 samples = 1 value = [0, 1] 1786->1787 1788 entropy = 1.0 samples = 2 value = [1, 1] 1786->1788 1791 hours-per-week <= 49.0 entropy = 1.0 samples = 4 value = [2, 2] 1790->1791 1794 hours-per-week <= 49.0 entropy = 0.863 samples = 7 value = [2, 5] 1790->1794 1792 entropy = 0.0 samples = 1 value = [1, 0] 1791->1792 1793 entropy = 0.918 samples = 3 value = [1, 2] 1791->1793 1795 entropy = 0.0 samples = 1 value = [0, 1] 1794->1795 1796 age <= 40.5 entropy = 0.918 samples = 6 value = [2, 4] 1794->1796 1797 entropy = 0.918 samples = 3 value = [1, 2] 1796->1797 1798 entropy = 0.918 samples = 3 value = [1, 2] 1796->1798 1800 entropy = 0.0 samples = 2 value = [2, 0] 1799->1800 1801 age <= 39.5 entropy = 1.0 samples = 2 value = [1, 1] 1799->1801 1802 entropy = 0.0 samples = 1 value = [0, 1] 1801->1802 1803 entropy = 0.0 samples = 1 value = [1, 0] 1801->1803 1805 age <= 42.5 entropy = 0.918 samples = 6 value = [4, 2] 1804->1805 1810 entropy = 0.0 samples = 1 value = [0, 1] 1804->1810 1806 entropy = 0.0 samples = 2 value = [2, 0] 1805->1806 1807 hours-per-week <= 49.0 entropy = 1.0 samples = 4 value = [2, 2] 1805->1807 1808 entropy = 0.0 samples = 1 value = [1, 0] 1807->1808 1809 entropy = 0.918 samples = 3 value = [1, 2] 1807->1809 1812 entropy = 0.0 samples = 2 value = [2, 0] 1811->1812 1813 entropy = 0.0 samples = 1 value = [0, 1] 1811->1813 1817 entropy = 0.0 samples = 1 value = [0, 1] 1816->1817 1818 age <= 58.0 entropy = 0.852 samples = 18 value = [5, 13] 1816->1818 1819 age <= 55.0 entropy = 0.811 samples = 16 value = [4, 12] 1818->1819 1832 entropy = 1.0 samples = 2 value = [1, 1] 1818->1832 1820 age <= 53.5 entropy = 0.863 samples = 14 value = [4, 10] 1819->1820 1831 entropy = 0.0 samples = 2 value = [0, 2] 1819->1831 1821 age <= 49.5 entropy = 0.779 samples = 13 value = [3, 10] 1820->1821 1830 entropy = 0.0 samples = 1 value = [1, 0] 1820->1830 1822 age <= 48.5 entropy = 0.918 samples = 9 value = [3, 6] 1821->1822 1829 entropy = 0.0 samples = 4 value = [0, 4] 1821->1829 1823 age <= 47.5 entropy = 0.863 samples = 7 value = [2, 5] 1822->1823 1828 entropy = 1.0 samples = 2 value = [1, 1] 1822->1828 1824 age <= 46.5 entropy = 0.918 samples = 6 value = [2, 4] 1823->1824 1827 entropy = 0.0 samples = 1 value = [0, 1] 1823->1827 1825 entropy = 0.918 samples = 3 value = [1, 2] 1824->1825 1826 entropy = 0.918 samples = 3 value = [1, 2] 1824->1826 1834 entropy = 0.0 samples = 9 value = [0, 9] 1833->1834 1835 entropy = 0.0 samples = 1 value = [1, 0] 1833->1835 1837 age <= 52.5 entropy = 0.811 samples = 16 value = [12, 4] 1836->1837 1854 entropy = 0.0 samples = 2 value = [2, 0] 1836->1854 1838 age <= 48.0 entropy = 0.863 samples = 14 value = [10, 4] 1837->1838 1853 entropy = 0.0 samples = 2 value = [2, 0] 1837->1853 1839 hours-per-week <= 55.5 entropy = 0.722 samples = 10 value = [8, 2] 1838->1839 1848 hours-per-week <= 55.5 entropy = 1.0 samples = 4 value = [2, 2] 1838->1848 1840 age <= 38.5 entropy = 0.544 samples = 8 value = [7, 1] 1839->1840 1845 age <= 43.5 entropy = 1.0 samples = 2 value = [1, 1] 1839->1845 1841 race_White <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 1840->1841 1844 entropy = 0.0 samples = 5 value = [5, 0] 1840->1844 1842 entropy = 0.0 samples = 1 value = [1, 0] 1841->1842 1843 entropy = 1.0 samples = 2 value = [1, 1] 1841->1843 1846 entropy = 0.0 samples = 1 value = [0, 1] 1845->1846 1847 entropy = 0.0 samples = 1 value = [1, 0] 1845->1847 1849 education <= 10.5 entropy = 0.918 samples = 3 value = [1, 2] 1848->1849 1852 entropy = 0.0 samples = 1 value = [1, 0] 1848->1852 1850 entropy = 1.0 samples = 2 value = [1, 1] 1849->1850 1851 entropy = 0.0 samples = 1 value = [0, 1] 1849->1851 1856 sex_Female <= 0.5 entropy = 0.684 samples = 22 value = [4, 18] 1855->1856 1871 entropy = 0.0 samples = 1 value = [1, 0] 1855->1871 1857 hours-per-week <= 67.5 entropy = 0.592 samples = 21 value = [3, 18] 1856->1857 1870 entropy = 0.0 samples = 1 value = [1, 0] 1856->1870 1858 race_White <= 0.5 entropy = 0.722 samples = 15 value = [3, 12] 1857->1858 1869 entropy = 0.0 samples = 6 value = [0, 6] 1857->1869 1859 entropy = 0.0 samples = 1 value = [1, 0] 1858->1859 1860 age <= 51.0 entropy = 0.592 samples = 14 value = [2, 12] 1858->1860 1861 age <= 47.5 entropy = 0.684 samples = 11 value = [2, 9] 1860->1861 1868 entropy = 0.0 samples = 3 value = [0, 3] 1860->1868 1862 hours-per-week <= 62.5 entropy = 0.503 samples = 9 value = [1, 8] 1861->1862 1867 entropy = 1.0 samples = 2 value = [1, 1] 1861->1867 1863 entropy = 0.0 samples = 6 value = [0, 6] 1862->1863 1864 age <= 41.5 entropy = 0.918 samples = 3 value = [1, 2] 1862->1864 1865 entropy = 1.0 samples = 2 value = [1, 1] 1864->1865 1866 entropy = 0.0 samples = 1 value = [0, 1] 1864->1866 1875 sex_Male <= 0.5 entropy = 0.837 samples = 90 value = [66, 24] 1874->1875 1948 entropy = 0.0 samples = 11 value = [11, 0] 1874->1948 1876 education <= 9.5 entropy = 0.454 samples = 21 value = [19, 2] 1875->1876 1887 age <= 76.0 entropy = 0.903 samples = 69 value = [47, 22] 1875->1887 1877 workclass_Self-emp <= 0.5 entropy = 0.619 samples = 13 value = [11, 2] 1876->1877 1886 entropy = 0.0 samples = 8 value = [8, 0] 1876->1886 1878 age <= 73.5 entropy = 0.439 samples = 11 value = [10, 1] 1877->1878 1883 age <= 67.5 entropy = 1.0 samples = 2 value = [1, 1] 1877->1883 1879 entropy = 0.0 samples = 8 value = [8, 0] 1878->1879 1880 age <= 74.5 entropy = 0.918 samples = 3 value = [2, 1] 1878->1880 1881 entropy = 0.0 samples = 1 value = [0, 1] 1880->1881 1882 entropy = 0.0 samples = 2 value = [2, 0] 1880->1882 1884 entropy = 0.0 samples = 1 value = [0, 1] 1883->1884 1885 entropy = 0.0 samples = 1 value = [1, 0] 1883->1885 1888 hours-per-week <= 36.0 entropy = 0.923 samples = 65 value = [43, 22] 1887->1888 1947 entropy = 0.0 samples = 4 value = [4, 0] 1887->1947 1889 education <= 9.5 entropy = 0.811 samples = 4 value = [1, 3] 1888->1889 1892 age <= 65.5 entropy = 0.895 samples = 61 value = [42, 19] 1888->1892 1890 entropy = 0.0 samples = 1 value = [1, 0] 1889->1890 1891 entropy = 0.0 samples = 3 value = [0, 3] 1889->1891 1893 hours-per-week <= 62.5 entropy = 0.967 samples = 33 value = [20, 13] 1892->1893 1922 hours-per-week <= 46.5 entropy = 0.75 samples = 28 value = [22, 6] 1892->1922 1894 workclass_Private <= 0.5 entropy = 0.981 samples = 31 value = [18, 13] 1893->1894 1921 entropy = 0.0 samples = 2 value = [2, 0] 1893->1921 1895 hours-per-week <= 52.0 entropy = 0.918 samples = 9 value = [3, 6] 1894->1895 1906 race_Black <= 0.5 entropy = 0.902 samples = 22 value = [15, 7] 1894->1906 1896 hours-per-week <= 42.5 entropy = 0.954 samples = 8 value = [3, 5] 1895->1896 1905 entropy = 0.0 samples = 1 value = [0, 1] 1895->1905 1897 education <= 9.5 entropy = 0.918 samples = 6 value = [2, 4] 1896->1897 1902 education <= 9.5 entropy = 1.0 samples = 2 value = [1, 1] 1896->1902 1898 entropy = 0.0 samples = 2 value = [0, 2] 1897->1898 1899 education <= 10.5 entropy = 1.0 samples = 4 value = [2, 2] 1897->1899 1900 entropy = 1.0 samples = 2 value = [1, 1] 1899->1900 1901 entropy = 1.0 samples = 2 value = [1, 1] 1899->1901 1903 entropy = 0.0 samples = 1 value = [1, 0] 1902->1903 1904 entropy = 0.0 samples = 1 value = [0, 1] 1902->1904 1907 education <= 9.5 entropy = 0.863 samples = 21 value = [15, 6] 1906->1907 1920 entropy = 0.0 samples = 1 value = [0, 1] 1906->1920 1908 hours-per-week <= 54.0 entropy = 0.971 samples = 15 value = [9, 6] 1907->1908 1919 entropy = 0.0 samples = 6 value = [6, 0] 1907->1919 1909 hours-per-week <= 42.5 entropy = 0.94 samples = 14 value = [9, 5] 1908->1909 1918 entropy = 0.0 samples = 1 value = [0, 1] 1908->1918 1910 race_Asian <= 0.5 entropy = 0.994 samples = 11 value = [6, 5] 1909->1910 1917 entropy = 0.0 samples = 3 value = [3, 0] 1909->1917 1911 age <= 64.5 entropy = 1.0 samples = 10 value = [5, 5] 1910->1911 1916 entropy = 0.0 samples = 1 value = [1, 0] 1910->1916 1912 age <= 63.5 entropy = 0.985 samples = 7 value = [4, 3] 1911->1912 1915 entropy = 0.918 samples = 3 value = [1, 2] 1911->1915 1913 entropy = 1.0 samples = 4 value = [2, 2] 1912->1913 1914 entropy = 0.918 samples = 3 value = [2, 1] 1912->1914 1923 race_Asian <= 0.5 entropy = 0.845 samples = 22 value = [16, 6] 1922->1923 1946 entropy = 0.0 samples = 6 value = [6, 0] 1922->1946 1924 workclass_Self-emp <= 0.5 entropy = 0.792 samples = 21 value = [16, 5] 1923->1924 1945 entropy = 0.0 samples = 1 value = [0, 1] 1923->1945 1925 hours-per-week <= 42.5 entropy = 0.94 samples = 14 value = [9, 5] 1924->1925 1944 entropy = 0.0 samples = 7 value = [7, 0] 1924->1944 1926 age <= 73.5 entropy = 0.89 samples = 13 value = [9, 4] 1925->1926 1943 entropy = 0.0 samples = 1 value = [0, 1] 1925->1943 1927 age <= 71.0 entropy = 0.811 samples = 12 value = [9, 3] 1926->1927 1942 entropy = 0.0 samples = 1 value = [0, 1] 1926->1942 1928 hours-per-week <= 38.5 entropy = 0.881 samples = 10 value = [7, 3] 1927->1928 1941 entropy = 0.0 samples = 2 value = [2, 0] 1927->1941 1929 entropy = 0.0 samples = 1 value = [1, 0] 1928->1929 1930 age <= 69.5 entropy = 0.918 samples = 9 value = [6, 3] 1928->1930 1931 age <= 67.5 entropy = 0.811 samples = 8 value = [6, 2] 1930->1931 1940 entropy = 0.0 samples = 1 value = [0, 1] 1930->1940 1932 education <= 9.5 entropy = 0.971 samples = 5 value = [3, 2] 1931->1932 1939 entropy = 0.0 samples = 3 value = [3, 0] 1931->1939 1933 age <= 66.5 entropy = 1.0 samples = 2 value = [1, 1] 1932->1933 1936 age <= 66.5 entropy = 0.918 samples = 3 value = [2, 1] 1932->1936 1934 entropy = 0.0 samples = 1 value = [1, 0] 1933->1934 1935 entropy = 0.0 samples = 1 value = [0, 1] 1933->1935 1937 entropy = 1.0 samples = 2 value = [1, 1] 1936->1937 1938 entropy = 0.0 samples = 1 value = [1, 0] 1936->1938 1950 age <= 28.5 entropy = 0.974 samples = 794 value = [322, 472] 1949->1950 2559 age <= 33.5 entropy = 0.781 samples = 721 value = [167, 554] 1949->2559 1951 age <= 24.5 entropy = 0.784 samples = 60 value = [46, 14] 1950->1951 1988 hours-per-week <= 31.0 entropy = 0.955 samples = 734 value = [276, 458] 1950->1988 1952 entropy = 0.0 samples = 11 value = [11, 0] 1951->1952 1953 sex_Female <= 0.5 entropy = 0.863 samples = 49 value = [35, 14] 1951->1953 1954 education <= 12.5 entropy = 0.722 samples = 35 value = [28, 7] 1953->1954 1977 race_Black <= 0.5 entropy = 1.0 samples = 14 value = [7, 7] 1953->1977 1955 entropy = 0.0 samples = 7 value = [7, 0] 1954->1955 1956 education <= 13.5 entropy = 0.811 samples = 28 value = [21, 7] 1954->1956 1957 hours-per-week <= 34.0 entropy = 0.855 samples = 25 value = [18, 7] 1956->1957 1976 entropy = 0.0 samples = 3 value = [3, 0] 1956->1976 1958 entropy = 0.0 samples = 3 value = [3, 0] 1957->1958 1959 race_White <= 0.5 entropy = 0.902 samples = 22 value = [15, 7] 1957->1959 1960 entropy = 0.0 samples = 1 value = [1, 0] 1959->1960 1961 hours-per-week <= 40.5 entropy = 0.918 samples = 21 value = [14, 7] 1959->1961 1962 workclass_Private <= 0.5 entropy = 0.934 samples = 20 value = [13, 7] 1961->1962 1975 entropy = 0.0 samples = 1 value = [1, 0] 1961->1975 1963 age <= 25.5 entropy = 0.918 samples = 3 value = [1, 2] 1962->1963 1968 age <= 25.5 entropy = 0.874 samples = 17 value = [12, 5] 1962->1968 1964 entropy = 0.0 samples = 1 value = [0, 1] 1963->1964 1965 age <= 27.0 entropy = 1.0 samples = 2 value = [1, 1] 1963->1965 1966 entropy = 0.0 samples = 1 value = [1, 0] 1965->1966 1967 entropy = 0.0 samples = 1 value = [0, 1] 1965->1967 1969 entropy = 0.0 samples = 2 value = [2, 0] 1968->1969 1970 age <= 26.5 entropy = 0.918 samples = 15 value = [10, 5] 1968->1970 1971 entropy = 0.918 samples = 3 value = [1, 2] 1970->1971 1972 age <= 27.5 entropy = 0.811 samples = 12 value = [9, 3] 1970->1972 1973 entropy = 0.722 samples = 5 value = [4, 1] 1972->1973 1974 entropy = 0.863 samples = 7 value = [5, 2] 1972->1974 1978 age <= 26.5 entropy = 0.98 samples = 12 value = [5, 7] 1977->1978 1987 entropy = 0.0 samples = 2 value = [2, 0] 1977->1987 1979 hours-per-week <= 35.0 entropy = 0.863 samples = 7 value = [5, 2] 1978->1979 1986 entropy = 0.0 samples = 5 value = [0, 5] 1978->1986 1980 entropy = 0.0 samples = 1 value = [0, 1] 1979->1980 1981 age <= 25.5 entropy = 0.65 samples = 6 value = [5, 1] 1979->1981 1982 entropy = 0.0 samples = 3 value = [3, 0] 1981->1982 1983 education <= 13.5 entropy = 0.918 samples = 3 value = [2, 1] 1981->1983 1984 entropy = 0.0 samples = 1 value = [0, 1] 1983->1984 1985 entropy = 0.0 samples = 2 value = [2, 0] 1983->1985 1989 sex_Male <= 0.5 entropy = 0.97 samples = 108 value = [65, 43] 1988->1989 2084 age <= 36.5 entropy = 0.922 samples = 626 value = [211, 415] 1988->2084 1990 age <= 67.5 entropy = 0.998 samples = 38 value = [18, 20] 1989->1990 2031 education <= 14.5 entropy = 0.913 samples = 70 value = [47, 23] 1989->2031 1991 education <= 14.5 entropy = 0.977 samples = 34 value = [14, 20] 1990->1991 2030 entropy = 0.0 samples = 4 value = [4, 0] 1990->2030 1992 education <= 13.5 entropy = 0.954 samples = 32 value = [12, 20] 1991->1992 2029 entropy = 0.0 samples = 2 value = [2, 0] 1991->2029 1993 hours-per-week <= 11.5 entropy = 0.985 samples = 28 value = [12, 16] 1992->1993 2028 entropy = 0.0 samples = 4 value = [0, 4] 1992->2028 1994 entropy = 0.0 samples = 2 value = [0, 2] 1993->1994 1995 age <= 51.5 entropy = 0.996 samples = 26 value = [12, 14] 1993->1995 1996 age <= 45.5 entropy = 0.976 samples = 22 value = [9, 13] 1995->1996 2023 age <= 60.5 entropy = 0.811 samples = 4 value = [3, 1] 1995->2023 1997 hours-per-week <= 24.5 entropy = 0.993 samples = 20 value = [9, 11] 1996->1997 2022 entropy = 0.0 samples = 2 value = [0, 2] 1996->2022 1998 hours-per-week <= 22.0 entropy = 0.954 samples = 8 value = [5, 3] 1997->1998 2009 age <= 42.0 entropy = 0.918 samples = 12 value = [4, 8] 1997->2009 1999 workclass_Private <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 1998->1999 2008 entropy = 0.0 samples = 2 value = [2, 0] 1998->2008 2000 hours-per-week <= 17.5 entropy = 0.811 samples = 4 value = [3, 1] 1999->2000 2007 entropy = 0.0 samples = 2 value = [0, 2] 1999->2007 2001 entropy = 0.0 samples = 1 value = [1, 0] 2000->2001 2002 age <= 34.5 entropy = 0.918 samples = 3 value = [2, 1] 2000->2002 2003 entropy = 0.0 samples = 1 value = [1, 0] 2002->2003 2004 age <= 39.5 entropy = 1.0 samples = 2 value = [1, 1] 2002->2004 2005 entropy = 0.0 samples = 1 value = [0, 1] 2004->2005 2006 entropy = 0.0 samples = 1 value = [1, 0] 2004->2006 2010 race_Black <= 0.5 entropy = 0.845 samples = 11 value = [3, 8] 2009->2010 2021 entropy = 0.0 samples = 1 value = [1, 0] 2009->2021 2011 age <= 36.5 entropy = 0.722 samples = 10 value = [2, 8] 2010->2011 2020 entropy = 0.0 samples = 1 value = [1, 0] 2010->2020 2012 entropy = 0.0 samples = 4 value = [0, 4] 2011->2012 2013 education <= 12.5 entropy = 0.918 samples = 6 value = [2, 4] 2011->2013 2014 entropy = 0.0 samples = 1 value = [1, 0] 2013->2014 2015 hours-per-week <= 27.5 entropy = 0.722 samples = 5 value = [1, 4] 2013->2015 2016 entropy = 0.0 samples = 2 value = [0, 2] 2015->2016 2017 age <= 38.0 entropy = 0.918 samples = 3 value = [1, 2] 2015->2017 2018 entropy = 0.0 samples = 2 value = [0, 2] 2017->2018 2019 entropy = 0.0 samples = 1 value = [1, 0] 2017->2019 2024 entropy = 0.0 samples = 2 value = [2, 0] 2023->2024 2025 hours-per-week <= 27.5 entropy = 1.0 samples = 2 value = [1, 1] 2023->2025 2026 entropy = 0.0 samples = 1 value = [1, 0] 2025->2026 2027 entropy = 0.0 samples = 1 value = [0, 1] 2025->2027 2032 age <= 42.5 entropy = 0.811 samples = 56 value = [42, 14] 2031->2032 2071 hours-per-week <= 27.0 entropy = 0.94 samples = 14 value = [5, 9] 2031->2071 2033 age <= 33.5 entropy = 0.323 samples = 17 value = [16, 1] 2032->2033 2040 education <= 12.5 entropy = 0.918 samples = 39 value = [26, 13] 2032->2040 2034 age <= 32.5 entropy = 0.544 samples = 8 value = [7, 1] 2033->2034 2039 entropy = 0.0 samples = 9 value = [9, 0] 2033->2039 2035 entropy = 0.0 samples = 5 value = [5, 0] 2034->2035 2036 hours-per-week <= 22.5 entropy = 0.918 samples = 3 value = [2, 1] 2034->2036 2037 entropy = 0.0 samples = 2 value = [2, 0] 2036->2037 2038 entropy = 0.0 samples = 1 value = [0, 1] 2036->2038 2041 entropy = 0.0 samples = 4 value = [4, 0] 2040->2041 2042 workclass_Self-emp <= 0.5 entropy = 0.952 samples = 35 value = [22, 13] 2040->2042 2043 hours-per-week <= 28.0 entropy = 0.998 samples = 17 value = [8, 9] 2042->2043 2056 age <= 62.0 entropy = 0.764 samples = 18 value = [14, 4] 2042->2056 2044 hours-per-week <= 10.5 entropy = 0.94 samples = 14 value = [5, 9] 2043->2044 2055 entropy = 0.0 samples = 3 value = [3, 0] 2043->2055 2045 entropy = 0.0 samples = 3 value = [0, 3] 2044->2045 2046 workclass_Private <= 0.5 entropy = 0.994 samples = 11 value = [5, 6] 2044->2046 2047 entropy = 0.0 samples = 2 value = [2, 0] 2046->2047 2048 hours-per-week <= 13.5 entropy = 0.918 samples = 9 value = [3, 6] 2046->2048 2049 entropy = 0.0 samples = 1 value = [1, 0] 2048->2049 2050 hours-per-week <= 22.0 entropy = 0.811 samples = 8 value = [2, 6] 2048->2050 2051 entropy = 0.0 samples = 5 value = [0, 5] 2050->2051 2052 age <= 50.0 entropy = 0.918 samples = 3 value = [2, 1] 2050->2052 2053 entropy = 0.0 samples = 1 value = [0, 1] 2052->2053 2054 entropy = 0.0 samples = 2 value = [2, 0] 2052->2054 2057 entropy = 0.0 samples = 8 value = [8, 0] 2056->2057 2058 age <= 63.5 entropy = 0.971 samples = 10 value = [6, 4] 2056->2058 2059 entropy = 0.0 samples = 1 value = [0, 1] 2058->2059 2060 age <= 66.0 entropy = 0.918 samples = 9 value = [6, 3] 2058->2060 2061 entropy = 0.0 samples = 2 value = [2, 0] 2060->2061 2062 age <= 68.0 entropy = 0.985 samples = 7 value = [4, 3] 2060->2062 2063 entropy = 0.0 samples = 1 value = [0, 1] 2062->2063 2064 hours-per-week <= 11.0 entropy = 0.918 samples = 6 value = [4, 2] 2062->2064 2065 entropy = 0.0 samples = 2 value = [2, 0] 2064->2065 2066 age <= 69.5 entropy = 1.0 samples = 4 value = [2, 2] 2064->2066 2067 entropy = 0.0 samples = 1 value = [1, 0] 2066->2067 2068 age <= 80.0 entropy = 0.918 samples = 3 value = [1, 2] 2066->2068 2069 entropy = 0.0 samples = 2 value = [0, 2] 2068->2069 2070 entropy = 0.0 samples = 1 value = [1, 0] 2068->2070 2072 education <= 15.5 entropy = 1.0 samples = 10 value = [5, 5] 2071->2072 2083 entropy = 0.0 samples = 4 value = [0, 4] 2071->2083 2073 workclass_Self-emp <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 2072->2073 2078 hours-per-week <= 11.5 entropy = 0.722 samples = 5 value = [1, 4] 2072->2078 2074 age <= 58.5 entropy = 1.0 samples = 2 value = [1, 1] 2073->2074 2077 entropy = 0.0 samples = 3 value = [3, 0] 2073->2077 2075 entropy = 0.0 samples = 1 value = [1, 0] 2074->2075 2076 entropy = 0.0 samples = 1 value = [0, 1] 2074->2076 2079 age <= 66.5 entropy = 1.0 samples = 2 value = [1, 1] 2078->2079 2082 entropy = 0.0 samples = 3 value = [0, 3] 2078->2082 2080 entropy = 0.0 samples = 1 value = [0, 1] 2079->2080 2081 entropy = 0.0 samples = 1 value = [1, 0] 2079->2081 2085 race_Hispanic <= 0.5 entropy = 0.988 samples = 163 value = [71, 92] 2084->2085 2212 race_Asian <= 0.5 entropy = 0.884 samples = 463 value = [140, 323] 2084->2212 2086 hours-per-week <= 34.0 entropy = 0.985 samples = 161 value = [69, 92] 2085->2086 2211 entropy = 0.0 samples = 2 value = [2, 0] 2085->2211 2087 entropy = 0.0 samples = 2 value = [0, 2] 2086->2087 2088 sex_Male <= 0.5 entropy = 0.987 samples = 159 value = [69, 90] 2086->2088 2089 age <= 30.5 entropy = 0.918 samples = 27 value = [9, 18] 2088->2089 2114 hours-per-week <= 35.5 entropy = 0.994 samples = 132 value = [60, 72] 2088->2114 2090 entropy = 0.0 samples = 5 value = [0, 5] 2089->2090 2091 age <= 35.5 entropy = 0.976 samples = 22 value = [9, 13] 2089->2091 2092 age <= 32.5 entropy = 0.998 samples = 19 value = [9, 10] 2091->2092 2113 entropy = 0.0 samples = 3 value = [0, 3] 2091->2113 2093 age <= 31.5 entropy = 0.811 samples = 8 value = [2, 6] 2092->2093 2100 hours-per-week <= 37.5 entropy = 0.946 samples = 11 value = [7, 4] 2092->2100 2094 education <= 12.5 entropy = 0.971 samples = 5 value = [2, 3] 2093->2094 2099 entropy = 0.0 samples = 3 value = [0, 3] 2093->2099 2095 entropy = 0.0 samples = 1 value = [1, 0] 2094->2095 2096 hours-per-week <= 37.5 entropy = 0.811 samples = 4 value = [1, 3] 2094->2096 2097 entropy = 0.0 samples = 2 value = [0, 2] 2096->2097 2098 entropy = 1.0 samples = 2 value = [1, 1] 2096->2098 2101 entropy = 0.0 samples = 1 value = [0, 1] 2100->2101 2102 age <= 34.5 entropy = 0.881 samples = 10 value = [7, 3] 2100->2102 2103 race_Black <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] 2102->2103 2112 entropy = 0.0 samples = 2 value = [2, 0] 2102->2112 2104 workclass_Private <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 2103->2104 2111 entropy = 0.0 samples = 1 value = [0, 1] 2103->2111 2105 entropy = 0.0 samples = 2 value = [2, 0] 2104->2105 2106 education <= 12.5 entropy = 0.971 samples = 5 value = [3, 2] 2104->2106 2107 entropy = 0.0 samples = 1 value = [1, 0] 2106->2107 2108 age <= 33.5 entropy = 1.0 samples = 4 value = [2, 2] 2106->2108 2109 entropy = 1.0 samples = 2 value = [1, 1] 2108->2109 2110 entropy = 1.0 samples = 2 value = [1, 1] 2108->2110 2115 race_Asian <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 2114->2115 2120 workclass_Public <= 0.5 entropy = 0.99 samples = 127 value = [56, 71] 2114->2120 2116 entropy = 0.0 samples = 3 value = [3, 0] 2115->2116 2117 age <= 34.5 entropy = 1.0 samples = 2 value = [1, 1] 2115->2117 2118 entropy = 0.0 samples = 1 value = [0, 1] 2117->2118 2119 entropy = 0.0 samples = 1 value = [1, 0] 2117->2119 2121 hours-per-week <= 37.5 entropy = 0.981 samples = 105 value = [44, 61] 2120->2121 2190 age <= 33.5 entropy = 0.994 samples = 22 value = [12, 10] 2120->2190 2122 entropy = 0.0 samples = 2 value = [0, 2] 2121->2122 2123 age <= 34.5 entropy = 0.985 samples = 103 value = [44, 59] 2121->2123 2124 age <= 33.5 entropy = 0.969 samples = 78 value = [31, 47] 2123->2124 2169 education <= 15.0 entropy = 0.999 samples = 25 value = [13, 12] 2123->2169 2125 education <= 14.5 entropy = 0.987 samples = 67 value = [29, 38] 2124->2125 2164 education <= 13.5 entropy = 0.684 samples = 11 value = [2, 9] 2124->2164 2126 education <= 13.5 entropy = 0.976 samples = 61 value = [25, 36] 2125->2126 2157 race_Asian <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 2125->2157 2127 hours-per-week <= 39.0 entropy = 0.987 samples = 53 value = [23, 30] 2126->2127 2152 workclass_Private <= 0.5 entropy = 0.811 samples = 8 value = [2, 6] 2126->2152 2128 entropy = 0.0 samples = 1 value = [0, 1] 2127->2128 2129 race_Asian <= 0.5 entropy = 0.99 samples = 52 value = [23, 29] 2127->2129 2130 age <= 30.5 entropy = 0.981 samples = 50 value = [21, 29] 2129->2130 2151 entropy = 0.0 samples = 2 value = [2, 0] 2129->2151 2131 age <= 29.5 entropy = 0.997 samples = 15 value = [7, 8] 2130->2131 2134 age <= 31.5 entropy = 0.971 samples = 35 value = [14, 21] 2130->2134 2132 entropy = 0.954 samples = 8 value = [3, 5] 2131->2132 2133 entropy = 0.985 samples = 7 value = [4, 3] 2131->2133 2135 education <= 12.5 entropy = 0.881 samples = 10 value = [3, 7] 2134->2135 2138 education <= 12.5 entropy = 0.99 samples = 25 value = [11, 14] 2134->2138 2136 entropy = 0.0 samples = 2 value = [0, 2] 2135->2136 2137 entropy = 0.954 samples = 8 value = [3, 5] 2135->2137 2139 entropy = 0.0 samples = 2 value = [2, 0] 2138->2139 2140 race_Black <= 0.5 entropy = 0.966 samples = 23 value = [9, 14] 2138->2140 2141 age <= 32.5 entropy = 0.959 samples = 21 value = [8, 13] 2140->2141 2148 age <= 32.5 entropy = 1.0 samples = 2 value = [1, 1] 2140->2148 2142 workclass_Self-emp <= 0.5 entropy = 0.994 samples = 11 value = [5, 6] 2141->2142 2145 workclass_Private <= 0.5 entropy = 0.881 samples = 10 value = [3, 7] 2141->2145 2143 entropy = 0.971 samples = 10 value = [4, 6] 2142->2143 2144 entropy = 0.0 samples = 1 value = [1, 0] 2142->2144 2146 entropy = 0.0 samples = 2 value = [0, 2] 2145->2146 2147 entropy = 0.954 samples = 8 value = [3, 5] 2145->2147 2149 entropy = 0.0 samples = 1 value = [0, 1] 2148->2149 2150 entropy = 0.0 samples = 1 value = [1, 0] 2148->2150 2153 entropy = 0.0 samples = 1 value = [1, 0] 2152->2153 2154 hours-per-week <= 39.0 entropy = 0.592 samples = 7 value = [1, 6] 2152->2154 2155 entropy = 0.0 samples = 1 value = [1, 0] 2154->2155 2156 entropy = 0.0 samples = 6 value = [0, 6] 2154->2156 2158 workclass_Self-emp <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 2157->2158 2163 entropy = 0.0 samples = 1 value = [0, 1] 2157->2163 2159 entropy = 0.0 samples = 3 value = [3, 0] 2158->2159 2160 education <= 15.5 entropy = 1.0 samples = 2 value = [1, 1] 2158->2160 2161 entropy = 0.0 samples = 1 value = [0, 1] 2160->2161 2162 entropy = 0.0 samples = 1 value = [1, 0] 2160->2162 2165 entropy = 0.503 samples = 9 value = [1, 8] 2164->2165 2166 workclass_Private <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 2164->2166 2167 entropy = 0.0 samples = 1 value = [0, 1] 2166->2167 2168 entropy = 0.0 samples = 1 value = [1, 0] 2166->2168 2170 hours-per-week <= 39.0 entropy = 0.988 samples = 23 value = [13, 10] 2169->2170 2189 entropy = 0.0 samples = 2 value = [0, 2] 2169->2189 2171 entropy = 0.0 samples = 1 value = [1, 0] 2170->2171 2172 workclass_Self-emp <= 0.5 entropy = 0.994 samples = 22 value = [12, 10] 2170->2172 2173 education <= 12.5 entropy = 0.982 samples = 19 value = [11, 8] 2172->2173 2184 education <= 13.0 entropy = 0.918 samples = 3 value = [1, 2] 2172->2184 2174 age <= 35.5 entropy = 0.918 samples = 3 value = [1, 2] 2173->2174 2177 age <= 35.5 entropy = 0.954 samples = 16 value = [10, 6] 2173->2177 2175 entropy = 0.0 samples = 2 value = [0, 2] 2174->2175 2176 entropy = 0.0 samples = 1 value = [1, 0] 2174->2176 2178 education <= 13.5 entropy = 0.811 samples = 8 value = [6, 2] 2177->2178 2181 race_White <= 0.5 entropy = 1.0 samples = 8 value = [4, 4] 2177->2181 2179 entropy = 0.0 samples = 4 value = [4, 0] 2178->2179 2180 entropy = 1.0 samples = 4 value = [2, 2] 2178->2180 2182 entropy = 1.0 samples = 2 value = [1, 1] 2181->2182 2183 entropy = 1.0 samples = 6 value = [3, 3] 2181->2183 2185 age <= 35.5 entropy = 1.0 samples = 2 value = [1, 1] 2184->2185 2188 entropy = 0.0 samples = 1 value = [0, 1] 2184->2188 2186 entropy = 0.0 samples = 1 value = [1, 0] 2185->2186 2187 entropy = 0.0 samples = 1 value = [0, 1] 2185->2187 2191 education <= 13.5 entropy = 0.918 samples = 15 value = [10, 5] 2190->2191 2206 age <= 34.5 entropy = 0.863 samples = 7 value = [2, 5] 2190->2206 2192 age <= 30.5 entropy = 0.98 samples = 12 value = [7, 5] 2191->2192 2205 entropy = 0.0 samples = 3 value = [3, 0] 2191->2205 2193 age <= 29.5 entropy = 0.811 samples = 4 value = [1, 3] 2192->2193 2198 race_White <= 0.5 entropy = 0.811 samples = 8 value = [6, 2] 2192->2198 2194 entropy = 0.0 samples = 1 value = [0, 1] 2193->2194 2195 race_White <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 2193->2195 2196 entropy = 0.0 samples = 1 value = [0, 1] 2195->2196 2197 entropy = 1.0 samples = 2 value = [1, 1] 2195->2197 2199 entropy = 0.0 samples = 2 value = [2, 0] 2198->2199 2200 education <= 12.5 entropy = 0.918 samples = 6 value = [4, 2] 2198->2200 2201 entropy = 0.0 samples = 2 value = [2, 0] 2200->2201 2202 age <= 31.5 entropy = 1.0 samples = 4 value = [2, 2] 2200->2202 2203 entropy = 1.0 samples = 2 value = [1, 1] 2202->2203 2204 entropy = 1.0 samples = 2 value = [1, 1] 2202->2204 2207 entropy = 0.0 samples = 4 value = [0, 4] 2206->2207 2208 age <= 35.5 entropy = 0.918 samples = 3 value = [2, 1] 2206->2208 2209 entropy = 1.0 samples = 2 value = [1, 1] 2208->2209 2210 entropy = 0.0 samples = 1 value = [1, 0] 2208->2210 2213 sex_Female <= 0.5 entropy = 0.86 samples = 431 value = [122, 309] 2212->2213 2528 sex_Male <= 0.5 entropy = 0.989 samples = 32 value = [18, 14] 2212->2528 2214 education <= 15.5 entropy = 0.83 samples = 370 value = [97, 273] 2213->2214 2469 age <= 53.0 entropy = 0.976 samples = 61 value = [25, 36] 2213->2469 2215 race_Amer-Indian <= 0.5 entropy = 0.846 samples = 355 value = [97, 258] 2214->2215 2468 entropy = 0.0 samples = 15 value = [0, 15] 2214->2468 2216 education <= 13.5 entropy = 0.84 samples = 353 value = [95, 258] 2215->2216 2467 entropy = 0.0 samples = 2 value = [2, 0] 2215->2467 2217 race_Hispanic <= 0.5 entropy = 0.886 samples = 237 value = [72, 165] 2216->2217 2382 age <= 82.0 entropy = 0.718 samples = 116 value = [23, 93] 2216->2382 2218 age <= 41.5 entropy = 0.882 samples = 236 value = [71, 165] 2217->2218 2381 entropy = 0.0 samples = 1 value = [1, 0] 2217->2381 2219 age <= 37.5 entropy = 0.96 samples = 60 value = [23, 37] 2218->2219 2254 workclass_Public <= 0.5 entropy = 0.845 samples = 176 value = [48, 128] 2218->2254 2220 education <= 12.5 entropy = 0.544 samples = 8 value = [1, 7] 2219->2220 2223 race_White <= 0.5 entropy = 0.983 samples = 52 value = [22, 30] 2219->2223 2221 entropy = 1.0 samples = 2 value = [1, 1] 2220->2221 2222 entropy = 0.0 samples = 6 value = [0, 6] 2220->2222 2224 entropy = 0.0 samples = 1 value = [1, 0] 2223->2224 2225 age <= 38.5 entropy = 0.977 samples = 51 value = [21, 30] 2223->2225 2226 workclass_Public <= 0.5 entropy = 0.9 samples = 19 value = [6, 13] 2225->2226 2233 hours-per-week <= 38.0 entropy = 0.997 samples = 32 value = [15, 17] 2225->2233 2227 hours-per-week <= 37.5 entropy = 0.852 samples = 18 value = [5, 13] 2226->2227 2232 entropy = 0.0 samples = 1 value = [1, 0] 2226->2232 2228 entropy = 0.0 samples = 1 value = [1, 0] 2227->2228 2229 workclass_Self-emp <= 0.5 entropy = 0.787 samples = 17 value = [4, 13] 2227->2229 2230 entropy = 0.837 samples = 15 value = [4, 11] 2229->2230 2231 entropy = 0.0 samples = 2 value = [0, 2] 2229->2231 2234 entropy = 0.0 samples = 2 value = [0, 2] 2233->2234 2235 workclass_Public <= 0.5 entropy = 1.0 samples = 30 value = [15, 15] 2233->2235 2236 education <= 12.5 entropy = 0.996 samples = 26 value = [14, 12] 2235->2236 2249 age <= 40.5 entropy = 0.811 samples = 4 value = [1, 3] 2235->2249 2237 entropy = 0.0 samples = 4 value = [4, 0] 2236->2237 2238 workclass_Private <= 0.5 entropy = 0.994 samples = 22 value = [10, 12] 2236->2238 2239 age <= 40.5 entropy = 0.971 samples = 5 value = [3, 2] 2238->2239 2244 age <= 40.5 entropy = 0.977 samples = 17 value = [7, 10] 2238->2244 2240 age <= 39.5 entropy = 1.0 samples = 4 value = [2, 2] 2239->2240 2243 entropy = 0.0 samples = 1 value = [1, 0] 2239->2243 2241 entropy = 1.0 samples = 2 value = [1, 1] 2240->2241 2242 entropy = 1.0 samples = 2 value = [1, 1] 2240->2242 2245 age <= 39.5 entropy = 0.994 samples = 11 value = [5, 6] 2244->2245 2248 entropy = 0.918 samples = 6 value = [2, 4] 2244->2248 2246 entropy = 0.985 samples = 7 value = [3, 4] 2245->2246 2247 entropy = 1.0 samples = 4 value = [2, 2] 2245->2247 2250 education <= 12.5 entropy = 1.0 samples = 2 value = [1, 1] 2249->2250 2253 entropy = 0.0 samples = 2 value = [0, 2] 2249->2253 2251 entropy = 0.0 samples = 1 value = [0, 1] 2250->2251 2252 entropy = 0.0 samples = 1 value = [1, 0] 2250->2252 2255 age <= 51.5 entropy = 0.778 samples = 126 value = [29, 97] 2254->2255 2338 age <= 60.5 entropy = 0.958 samples = 50 value = [19, 31] 2254->2338 2256 age <= 49.5 entropy = 0.7 samples = 74 value = [14, 60] 2255->2256 2299 education <= 12.5 entropy = 0.867 samples = 52 value = [15, 37] 2255->2299 2257 race_White <= 0.5 entropy = 0.752 samples = 65 value = [14, 51] 2256->2257 2298 entropy = 0.0 samples = 9 value = [0, 9] 2256->2298 2258 entropy = 0.0 samples = 4 value = [0, 4] 2257->2258 2259 workclass_Private <= 0.5 entropy = 0.777 samples = 61 value = [14, 47] 2257->2259 2260 age <= 44.5 entropy = 0.961 samples = 13 value = [5, 8] 2259->2260 2271 age <= 44.5 entropy = 0.696 samples = 48 value = [9, 39] 2259->2271 2261 entropy = 0.0 samples = 2 value = [2, 0] 2260->2261 2262 education <= 12.5 entropy = 0.845 samples = 11 value = [3, 8] 2260->2262 2263 entropy = 0.0 samples = 1 value = [1, 0] 2262->2263 2264 age <= 45.5 entropy = 0.722 samples = 10 value = [2, 8] 2262->2264 2265 entropy = 0.0 samples = 3 value = [0, 3] 2264->2265 2266 age <= 46.5 entropy = 0.863 samples = 7 value = [2, 5] 2264->2266 2267 entropy = 0.918 samples = 3 value = [1, 2] 2266->2267 2268 age <= 47.5 entropy = 0.811 samples = 4 value = [1, 3] 2266->2268 2269 entropy = 0.0 samples = 1 value = [0, 1] 2268->2269 2270 entropy = 0.918 samples = 3 value = [1, 2] 2268->2270 2272 age <= 43.5 entropy = 0.426 samples = 23 value = [2, 21] 2271->2272 2279 hours-per-week <= 36.0 entropy = 0.855 samples = 25 value = [7, 18] 2271->2279 2273 education <= 12.5 entropy = 0.567 samples = 15 value = [2, 13] 2272->2273 2278 entropy = 0.0 samples = 8 value = [0, 8] 2272->2278 2274 entropy = 0.0 samples = 1 value = [0, 1] 2273->2274 2275 age <= 42.5 entropy = 0.592 samples = 14 value = [2, 12] 2273->2275 2276 entropy = 0.65 samples = 6 value = [1, 5] 2275->2276 2277 entropy = 0.544 samples = 8 value = [1, 7] 2275->2277 2280 entropy = 0.0 samples = 1 value = [0, 1] 2279->2280 2281 hours-per-week <= 38.5 entropy = 0.871 samples = 24 value = [7, 17] 2279->2281 2282 entropy = 0.0 samples = 1 value = [1, 0] 2281->2282 2283 age <= 46.5 entropy = 0.828 samples = 23 value = [6, 17] 2281->2283 2284 education <= 12.5 entropy = 0.75 samples = 14 value = [3, 11] 2283->2284 2291 education <= 12.5 entropy = 0.918 samples = 9 value = [3, 6] 2283->2291 2285 age <= 45.5 entropy = 0.971 samples = 5 value = [2, 3] 2284->2285 2288 age <= 45.5 entropy = 0.503 samples = 9 value = [1, 8] 2284->2288 2286 entropy = 0.0 samples = 1 value = [1, 0] 2285->2286 2287 entropy = 0.811 samples = 4 value = [1, 3] 2285->2287 2289 entropy = 0.0 samples = 4 value = [0, 4] 2288->2289 2290 entropy = 0.722 samples = 5 value = [1, 4] 2288->2290 2292 entropy = 0.0 samples = 1 value = [0, 1] 2291->2292 2293 age <= 48.5 entropy = 0.954 samples = 8 value = [3, 5] 2291->2293 2294 age <= 47.5 entropy = 1.0 samples = 4 value = [2, 2] 2293->2294 2297 entropy = 0.811 samples = 4 value = [1, 3] 2293->2297 2295 entropy = 1.0 samples = 2 value = [1, 1] 2294->2295 2296 entropy = 1.0 samples = 2 value = [1, 1] 2294->2296 2300 entropy = 0.0 samples = 5 value = [0, 5] 2299->2300 2301 age <= 65.0 entropy = 0.903 samples = 47 value = [15, 32] 2299->2301 2302 workclass_Private <= 0.5 entropy = 0.926 samples = 44 value = [15, 29] 2301->2302 2337 entropy = 0.0 samples = 3 value = [0, 3] 2301->2337 2303 age <= 53.5 entropy = 0.65 samples = 12 value = [2, 10] 2302->2303 2310 age <= 63.5 entropy = 0.974 samples = 32 value = [13, 19] 2302->2310 2304 entropy = 1.0 samples = 2 value = [1, 1] 2303->2304 2305 age <= 63.5 entropy = 0.469 samples = 10 value = [1, 9] 2303->2305 2306 entropy = 0.0 samples = 8 value = [0, 8] 2305->2306 2307 hours-per-week <= 37.5 entropy = 1.0 samples = 2 value = [1, 1] 2305->2307 2308 entropy = 0.0 samples = 1 value = [0, 1] 2307->2308 2309 entropy = 0.0 samples = 1 value = [1, 0] 2307->2309 2311 age <= 61.0 entropy = 0.987 samples = 30 value = [13, 17] 2310->2311 2336 entropy = 0.0 samples = 2 value = [0, 2] 2310->2336 2312 age <= 56.0 entropy = 0.954 samples = 24 value = [9, 15] 2311->2312 2331 hours-per-week <= 37.5 entropy = 0.918 samples = 6 value = [4, 2] 2311->2331 2313 hours-per-week <= 37.5 entropy = 1.0 samples = 12 value = [6, 6] 2312->2313 2326 age <= 57.5 entropy = 0.811 samples = 12 value = [3, 9] 2312->2326 2314 entropy = 0.0 samples = 1 value = [0, 1] 2313->2314 2315 age <= 53.5 entropy = 0.994 samples = 11 value = [6, 5] 2313->2315 2316 age <= 52.5 entropy = 0.971 samples = 5 value = [2, 3] 2315->2316 2323 age <= 54.5 entropy = 0.918 samples = 6 value = [4, 2] 2315->2323 2317 race_White <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 2316->2317 2320 race_Black <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 2316->2320 2318 entropy = 0.0 samples = 1 value = [0, 1] 2317->2318 2319 entropy = 0.0 samples = 1 value = [1, 0] 2317->2319 2321 entropy = 0.0 samples = 1 value = [0, 1] 2320->2321 2322 entropy = 1.0 samples = 2 value = [1, 1] 2320->2322 2324 entropy = 0.918 samples = 3 value = [2, 1] 2323->2324 2325 entropy = 0.918 samples = 3 value = [2, 1] 2323->2325 2327 entropy = 0.0 samples = 5 value = [0, 5] 2326->2327 2328 age <= 59.0 entropy = 0.985 samples = 7 value = [3, 4] 2326->2328 2329 entropy = 0.971 samples = 5 value = [2, 3] 2328->2329 2330 entropy = 1.0 samples = 2 value = [1, 1] 2328->2330 2332 entropy = 0.0 samples = 1 value = [1, 0] 2331->2332 2333 age <= 62.5 entropy = 0.971 samples = 5 value = [3, 2] 2331->2333 2334 entropy = 0.918 samples = 3 value = [2, 1] 2333->2334 2335 entropy = 1.0 samples = 2 value = [1, 1] 2333->2335 2339 age <= 56.5 entropy = 0.925 samples = 47 value = [16, 31] 2338->2339 2380 entropy = 0.0 samples = 3 value = [3, 0] 2338->2380 2340 education <= 12.5 entropy = 0.959 samples = 42 value = [16, 26] 2339->2340 2379 entropy = 0.0 samples = 5 value = [0, 5] 2339->2379 2341 age <= 49.0 entropy = 0.985 samples = 7 value = [4, 3] 2340->2341 2350 age <= 48.0 entropy = 0.928 samples = 35 value = [12, 23] 2340->2350 2342 race_White <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 2341->2342 2349 entropy = 0.0 samples = 2 value = [2, 0] 2341->2349 2343 entropy = 0.0 samples = 1 value = [0, 1] 2342->2343 2344 age <= 45.0 entropy = 1.0 samples = 4 value = [2, 2] 2342->2344 2345 entropy = 0.0 samples = 1 value = [0, 1] 2344->2345 2346 age <= 46.5 entropy = 0.918 samples = 3 value = [2, 1] 2344->2346 2347 entropy = 0.0 samples = 1 value = [1, 0] 2346->2347 2348 entropy = 1.0 samples = 2 value = [1, 1] 2346->2348 2351 age <= 46.5 entropy = 0.998 samples = 17 value = [8, 9] 2350->2351 2366 age <= 51.5 entropy = 0.764 samples = 18 value = [4, 14] 2350->2366 2352 age <= 42.5 entropy = 0.94 samples = 14 value = [5, 9] 2351->2352 2365 entropy = 0.0 samples = 3 value = [3, 0] 2351->2365 2353 entropy = 0.0 samples = 1 value = [0, 1] 2352->2353 2354 age <= 45.5 entropy = 0.961 samples = 13 value = [5, 8] 2352->2354 2355 age <= 44.0 entropy = 1.0 samples = 8 value = [4, 4] 2354->2355 2360 hours-per-week <= 38.5 entropy = 0.722 samples = 5 value = [1, 4] 2354->2360 2356 race_Black <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] 2355->2356 2359 entropy = 0.0 samples = 2 value = [2, 0] 2355->2359 2357 entropy = 0.971 samples = 5 value = [2, 3] 2356->2357 2358 entropy = 0.0 samples = 1 value = [0, 1] 2356->2358 2361 hours-per-week <= 36.0 entropy = 1.0 samples = 2 value = [1, 1] 2360->2361 2364 entropy = 0.0 samples = 3 value = [0, 3] 2360->2364 2362 entropy = 0.0 samples = 1 value = [0, 1] 2361->2362 2363 entropy = 0.0 samples = 1 value = [1, 0] 2361->2363 2367 entropy = 0.0 samples = 8 value = [0, 8] 2366->2367 2368 hours-per-week <= 36.5 entropy = 0.971 samples = 10 value = [4, 6] 2366->2368 2369 entropy = 0.0 samples = 1 value = [1, 0] 2368->2369 2370 age <= 54.5 entropy = 0.918 samples = 9 value = [3, 6] 2368->2370 2371 age <= 53.0 entropy = 1.0 samples = 4 value = [2, 2] 2370->2371 2376 age <= 55.5 entropy = 0.722 samples = 5 value = [1, 4] 2370->2376 2372 race_Black <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 2371->2372 2375 entropy = 0.0 samples = 1 value = [1, 0] 2371->2375 2373 entropy = 1.0 samples = 2 value = [1, 1] 2372->2373 2374 entropy = 0.0 samples = 1 value = [0, 1] 2372->2374 2377 entropy = 0.0 samples = 2 value = [0, 2] 2376->2377 2378 entropy = 0.918 samples = 3 value = [1, 2] 2376->2378 2383 age <= 37.5 entropy = 0.704 samples = 115 value = [22, 93] 2382->2383 2466 entropy = 0.0 samples = 1 value = [1, 0] 2382->2466 2384 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 2383->2384 2389 age <= 38.5 entropy = 0.677 samples = 112 value = [20, 92] 2383->2389 2385 entropy = 0.0 samples = 1 value = [1, 0] 2384->2385 2386 hours-per-week <= 39.0 entropy = 1.0 samples = 2 value = [1, 1] 2384->2386 2387 entropy = 0.0 samples = 1 value = [1, 0] 2386->2387 2388 entropy = 0.0 samples = 1 value = [0, 1] 2386->2388 2390 entropy = 0.0 samples = 9 value = [0, 9] 2389->2390 2391 age <= 41.5 entropy = 0.71 samples = 103 value = [20, 83] 2389->2391 2392 workclass_Self-emp <= 0.5 entropy = 0.874 samples = 17 value = [5, 12] 2391->2392 2405 age <= 43.5 entropy = 0.668 samples = 86 value = [15, 71] 2391->2405 2393 education <= 14.5 entropy = 0.811 samples = 16 value = [4, 12] 2392->2393 2404 entropy = 0.0 samples = 1 value = [1, 0] 2392->2404 2394 age <= 40.5 entropy = 0.89 samples = 13 value = [4, 9] 2393->2394 2403 entropy = 0.0 samples = 3 value = [0, 3] 2393->2403 2395 workclass_Public <= 0.5 entropy = 0.764 samples = 9 value = [2, 7] 2394->2395 2400 workclass_Private <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 2394->2400 2396 age <= 39.5 entropy = 0.863 samples = 7 value = [2, 5] 2395->2396 2399 entropy = 0.0 samples = 2 value = [0, 2] 2395->2399 2397 entropy = 0.811 samples = 4 value = [1, 3] 2396->2397 2398 entropy = 0.918 samples = 3 value = [1, 2] 2396->2398 2401 entropy = 1.0 samples = 2 value = [1, 1] 2400->2401 2402 entropy = 1.0 samples = 2 value = [1, 1] 2400->2402 2406 entropy = 0.0 samples = 12 value = [0, 12] 2405->2406 2407 age <= 51.5 entropy = 0.727 samples = 74 value = [15, 59] 2405->2407 2408 education <= 14.5 entropy = 0.65 samples = 48 value = [8, 40] 2407->2408 2443 age <= 52.5 entropy = 0.84 samples = 26 value = [7, 19] 2407->2443 2409 age <= 49.5 entropy = 0.712 samples = 41 value = [8, 33] 2408->2409 2442 entropy = 0.0 samples = 7 value = [0, 7] 2408->2442 2410 race_Black <= 0.5 entropy = 0.764 samples = 36 value = [8, 28] 2409->2410 2441 entropy = 0.0 samples = 5 value = [0, 5] 2409->2441 2411 workclass_Private <= 0.5 entropy = 0.734 samples = 34 value = [7, 27] 2410->2411 2438 workclass_Public <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 2410->2438 2412 age <= 47.5 entropy = 0.831 samples = 19 value = [5, 14] 2411->2412 2433 age <= 47.5 entropy = 0.567 samples = 15 value = [2, 13] 2411->2433 2413 hours-per-week <= 40.5 entropy = 0.94 samples = 14 value = [5, 9] 2412->2413 2432 entropy = 0.0 samples = 5 value = [0, 5] 2412->2432 2414 hours-per-week <= 36.5 entropy = 0.961 samples = 13 value = [5, 8] 2413->2414 2431 entropy = 0.0 samples = 1 value = [0, 1] 2413->2431 2415 age <= 45.5 entropy = 1.0 samples = 2 value = [1, 1] 2414->2415 2418 age <= 44.5 entropy = 0.946 samples = 11 value = [4, 7] 2414->2418 2416 entropy = 0.0 samples = 1 value = [1, 0] 2415->2416 2417 entropy = 0.0 samples = 1 value = [0, 1] 2415->2417 2419 workclass_Self-emp <= 0.5 entropy = 0.722 samples = 5 value = [1, 4] 2418->2419 2422 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 2418->2422 2420 entropy = 0.0 samples = 3 value = [0, 3] 2419->2420 2421 entropy = 1.0 samples = 2 value = [1, 1] 2419->2421 2423 hours-per-week <= 39.0 entropy = 0.971 samples = 5 value = [3, 2] 2422->2423 2430 entropy = 0.0 samples = 1 value = [0, 1] 2422->2430 2424 entropy = 0.0 samples = 1 value = [0, 1] 2423->2424 2425 age <= 45.5 entropy = 0.811 samples = 4 value = [3, 1] 2423->2425 2426 entropy = 0.0 samples = 1 value = [1, 0] 2425->2426 2427 age <= 46.5 entropy = 0.918 samples = 3 value = [2, 1] 2425->2427 2428 entropy = 1.0 samples = 2 value = [1, 1] 2427->2428 2429 entropy = 0.0 samples = 1 value = [1, 0] 2427->2429 2434 entropy = 0.0 samples = 10 value = [0, 10] 2433->2434 2435 age <= 48.5 entropy = 0.971 samples = 5 value = [2, 3] 2433->2435 2436 entropy = 0.918 samples = 3 value = [1, 2] 2435->2436 2437 entropy = 1.0 samples = 2 value = [1, 1] 2435->2437 2439 entropy = 0.0 samples = 1 value = [1, 0] 2438->2439 2440 entropy = 0.0 samples = 1 value = [0, 1] 2438->2440 2444 hours-per-week <= 37.5 entropy = 0.918 samples = 3 value = [2, 1] 2443->2444 2447 workclass_Public <= 0.5 entropy = 0.755 samples = 23 value = [5, 18] 2443->2447 2445 entropy = 0.0 samples = 1 value = [0, 1] 2444->2445 2446 entropy = 0.0 samples = 2 value = [2, 0] 2444->2446 2448 age <= 54.5 entropy = 0.874 samples = 17 value = [5, 12] 2447->2448 2465 entropy = 0.0 samples = 6 value = [0, 6] 2447->2465 2449 entropy = 0.0 samples = 1 value = [1, 0] 2448->2449 2450 age <= 56.0 entropy = 0.811 samples = 16 value = [4, 12] 2448->2450 2451 entropy = 0.0 samples = 3 value = [0, 3] 2450->2451 2452 age <= 60.5 entropy = 0.89 samples = 13 value = [4, 9] 2450->2452 2453 age <= 59.5 entropy = 0.971 samples = 5 value = [3, 2] 2452->2453 2460 age <= 73.0 entropy = 0.544 samples = 8 value = [1, 7] 2452->2460 2454 age <= 58.5 entropy = 1.0 samples = 4 value = [2, 2] 2453->2454 2459 entropy = 0.0 samples = 1 value = [1, 0] 2453->2459 2455 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 2454->2455 2458 entropy = 0.0 samples = 1 value = [0, 1] 2454->2458 2456 entropy = 0.0 samples = 1 value = [0, 1] 2455->2456 2457 entropy = 0.0 samples = 2 value = [2, 0] 2455->2457 2461 entropy = 0.0 samples = 6 value = [0, 6] 2460->2461 2462 hours-per-week <= 37.5 entropy = 1.0 samples = 2 value = [1, 1] 2460->2462 2463 entropy = 0.0 samples = 1 value = [1, 0] 2462->2463 2464 entropy = 0.0 samples = 1 value = [0, 1] 2462->2464 2470 education <= 12.5 entropy = 0.902 samples = 44 value = [14, 30] 2469->2470 2513 age <= 56.5 entropy = 0.937 samples = 17 value = [11, 6] 2469->2513 2471 hours-per-week <= 37.5 entropy = 0.439 samples = 11 value = [1, 10] 2470->2471 2476 workclass_Self-emp <= 0.5 entropy = 0.967 samples = 33 value = [13, 20] 2470->2476 2472 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 2471->2472 2475 entropy = 0.0 samples = 8 value = [0, 8] 2471->2475 2473 entropy = 0.0 samples = 1 value = [1, 0] 2472->2473 2474 entropy = 0.0 samples = 2 value = [0, 2] 2472->2474 2477 age <= 43.5 entropy = 0.938 samples = 31 value = [11, 20] 2476->2477 2512 entropy = 0.0 samples = 2 value = [2, 0] 2476->2512 2478 hours-per-week <= 38.5 entropy = 0.993 samples = 20 value = [9, 11] 2477->2478 2503 education <= 13.5 entropy = 0.684 samples = 11 value = [2, 9] 2477->2503 2479 age <= 37.5 entropy = 0.722 samples = 5 value = [1, 4] 2478->2479 2482 age <= 39.5 entropy = 0.997 samples = 15 value = [8, 7] 2478->2482 2480 entropy = 0.0 samples = 1 value = [1, 0] 2479->2480 2481 entropy = 0.0 samples = 4 value = [0, 4] 2479->2481 2483 education <= 13.5 entropy = 0.954 samples = 8 value = [3, 5] 2482->2483 2492 age <= 40.5 entropy = 0.863 samples = 7 value = [5, 2] 2482->2492 2484 age <= 37.5 entropy = 0.863 samples = 7 value = [2, 5] 2483->2484 2491 entropy = 0.0 samples = 1 value = [1, 0] 2483->2491 2485 entropy = 0.0 samples = 2 value = [0, 2] 2484->2485 2486 age <= 38.5 entropy = 0.971 samples = 5 value = [2, 3] 2484->2486 2487 workclass_Private <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 2486->2487 2490 entropy = 0.0 samples = 1 value = [0, 1] 2486->2490 2488 entropy = 0.0 samples = 1 value = [0, 1] 2487->2488 2489 entropy = 0.918 samples = 3 value = [2, 1] 2487->2489 2493 entropy = 0.0 samples = 2 value = [2, 0] 2492->2493 2494 race_Black <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] 2492->2494 2495 education <= 15.5 entropy = 1.0 samples = 4 value = [2, 2] 2494->2495 2502 entropy = 0.0 samples = 1 value = [1, 0] 2494->2502 2496 age <= 41.5 entropy = 0.918 samples = 3 value = [1, 2] 2495->2496 2501 entropy = 0.0 samples = 1 value = [1, 0] 2495->2501 2497 workclass_Private <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 2496->2497 2500 entropy = 0.0 samples = 1 value = [0, 1] 2496->2500 2498 entropy = 0.0 samples = 1 value = [1, 0] 2497->2498 2499 entropy = 0.0 samples = 1 value = [0, 1] 2497->2499 2504 entropy = 0.0 samples = 5 value = [0, 5] 2503->2504 2505 age <= 50.5 entropy = 0.918 samples = 6 value = [2, 4] 2503->2505 2506 age <= 46.5 entropy = 1.0 samples = 4 value = [2, 2] 2505->2506 2511 entropy = 0.0 samples = 2 value = [0, 2] 2505->2511 2507 entropy = 0.0 samples = 1 value = [0, 1] 2506->2507 2508 age <= 49.5 entropy = 0.918 samples = 3 value = [2, 1] 2506->2508 2509 entropy = 1.0 samples = 2 value = [1, 1] 2508->2509 2510 entropy = 0.0 samples = 1 value = [1, 0] 2508->2510 2514 entropy = 0.0 samples = 5 value = [5, 0] 2513->2514 2515 education <= 12.5 entropy = 1.0 samples = 12 value = [6, 6] 2513->2515 2516 entropy = 0.0 samples = 2 value = [2, 0] 2515->2516 2517 workclass_Self-emp <= 0.5 entropy = 0.971 samples = 10 value = [4, 6] 2515->2517 2518 hours-per-week <= 37.0 entropy = 1.0 samples = 8 value = [4, 4] 2517->2518 2527 entropy = 0.0 samples = 2 value = [0, 2] 2517->2527 2519 entropy = 0.0 samples = 1 value = [0, 1] 2518->2519 2520 age <= 61.5 entropy = 0.985 samples = 7 value = [4, 3] 2518->2520 2521 workclass_Public <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 2520->2521 2526 entropy = 0.0 samples = 2 value = [2, 0] 2520->2526 2522 age <= 60.0 entropy = 0.918 samples = 3 value = [2, 1] 2521->2522 2525 entropy = 0.0 samples = 2 value = [0, 2] 2521->2525 2523 entropy = 0.0 samples = 2 value = [2, 0] 2522->2523 2524 entropy = 0.0 samples = 1 value = [0, 1] 2522->2524 2529 entropy = 0.0 samples = 4 value = [4, 0] 2528->2529 2530 age <= 55.0 entropy = 1.0 samples = 28 value = [14, 14] 2528->2530 2531 education <= 13.5 entropy = 0.949 samples = 19 value = [7, 12] 2530->2531 2552 workclass_Self-emp <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] 2530->2552 2532 age <= 39.0 entropy = 1.0 samples = 12 value = [6, 6] 2531->2532 2547 age <= 38.5 entropy = 0.592 samples = 7 value = [1, 6] 2531->2547 2533 entropy = 0.0 samples = 1 value = [1, 0] 2532->2533 2534 age <= 44.5 entropy = 0.994 samples = 11 value = [5, 6] 2532->2534 2535 entropy = 0.0 samples = 2 value = [0, 2] 2534->2535 2536 age <= 46.5 entropy = 0.991 samples = 9 value = [5, 4] 2534->2536 2537 entropy = 0.0 samples = 2 value = [2, 0] 2536->2537 2538 age <= 52.0 entropy = 0.985 samples = 7 value = [3, 4] 2536->2538 2539 age <= 50.0 entropy = 0.918 samples = 6 value = [2, 4] 2538->2539 2546 entropy = 0.0 samples = 1 value = [1, 0] 2538->2546 2540 age <= 48.5 entropy = 0.971 samples = 5 value = [2, 3] 2539->2540 2545 entropy = 0.0 samples = 1 value = [0, 1] 2539->2545 2541 workclass_Self-emp <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 2540->2541 2544 entropy = 0.0 samples = 1 value = [1, 0] 2540->2544 2542 entropy = 0.0 samples = 3 value = [0, 3] 2541->2542 2543 entropy = 0.0 samples = 1 value = [1, 0] 2541->2543 2548 workclass_Public <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 2547->2548 2551 entropy = 0.0 samples = 5 value = [0, 5] 2547->2551 2549 entropy = 0.0 samples = 1 value = [0, 1] 2548->2549 2550 entropy = 0.0 samples = 1 value = [1, 0] 2548->2550 2553 education <= 14.5 entropy = 0.544 samples = 8 value = [7, 1] 2552->2553 2558 entropy = 0.0 samples = 1 value = [0, 1] 2552->2558 2554 entropy = 0.0 samples = 5 value = [5, 0] 2553->2554 2555 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 2553->2555 2556 entropy = 0.0 samples = 1 value = [0, 1] 2555->2556 2557 entropy = 0.0 samples = 2 value = [2, 0] 2555->2557 2560 hours-per-week <= 48.5 entropy = 0.958 samples = 137 value = [52, 85] 2559->2560 2695 education <= 14.5 entropy = 0.716 samples = 584 value = [115, 469] 2559->2695 2561 education <= 13.5 entropy = 0.828 samples = 46 value = [12, 34] 2560->2561 2596 age <= 24.5 entropy = 0.989 samples = 91 value = [40, 51] 2560->2596 2562 sex_Male <= 0.5 entropy = 0.881 samples = 40 value = [12, 28] 2561->2562 2595 entropy = 0.0 samples = 6 value = [0, 6] 2561->2595 2563 entropy = 0.0 samples = 5 value = [0, 5] 2562->2563 2564 workclass_Private <= 0.5 entropy = 0.928 samples = 35 value = [12, 23] 2562->2564 2565 education <= 12.5 entropy = 0.544 samples = 8 value = [1, 7] 2564->2565 2570 age <= 27.5 entropy = 0.975 samples = 27 value = [11, 16] 2564->2570 2566 age <= 28.0 entropy = 1.0 samples = 2 value = [1, 1] 2565->2566 2569 entropy = 0.0 samples = 6 value = [0, 6] 2565->2569 2567 entropy = 0.0 samples = 1 value = [0, 1] 2566->2567 2568 entropy = 0.0 samples = 1 value = [1, 0] 2566->2568 2571 age <= 24.5 entropy = 0.65 samples = 6 value = [1, 5] 2570->2571 2576 hours-per-week <= 44.0 entropy = 0.998 samples = 21 value = [10, 11] 2570->2576 2572 hours-per-week <= 44.5 entropy = 0.918 samples = 3 value = [1, 2] 2571->2572 2575 entropy = 0.0 samples = 3 value = [0, 3] 2571->2575 2573 entropy = 0.0 samples = 1 value = [0, 1] 2572->2573 2574 entropy = 1.0 samples = 2 value = [1, 1] 2572->2574 2577 race_White <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 2576->2577 2580 age <= 29.5 entropy = 0.977 samples = 17 value = [7, 10] 2576->2580 2578 entropy = 0.0 samples = 1 value = [0, 1] 2577->2578 2579 entropy = 0.0 samples = 3 value = [3, 0] 2577->2579 2581 education <= 12.5 entropy = 0.985 samples = 7 value = [4, 3] 2580->2581 2586 age <= 32.5 entropy = 0.881 samples = 10 value = [3, 7] 2580->2586 2582 entropy = 0.0 samples = 1 value = [0, 1] 2581->2582 2583 age <= 28.5 entropy = 0.918 samples = 6 value = [4, 2] 2581->2583 2584 entropy = 0.971 samples = 5 value = [3, 2] 2583->2584 2585 entropy = 0.0 samples = 1 value = [1, 0] 2583->2585 2587 age <= 30.5 entropy = 0.65 samples = 6 value = [1, 5] 2586->2587 2590 education <= 12.5 entropy = 1.0 samples = 4 value = [2, 2] 2586->2590 2588 entropy = 0.918 samples = 3 value = [1, 2] 2587->2588 2589 entropy = 0.0 samples = 3 value = [0, 3] 2587->2589 2591 entropy = 0.0 samples = 1 value = [1, 0] 2590->2591 2592 hours-per-week <= 46.0 entropy = 0.918 samples = 3 value = [1, 2] 2590->2592 2593 entropy = 1.0 samples = 2 value = [1, 1] 2592->2593 2594 entropy = 0.0 samples = 1 value = [0, 1] 2592->2594 2597 entropy = 0.0 samples = 2 value = [2, 0] 2596->2597 2598 race_Black <= 0.5 entropy = 0.985 samples = 89 value = [38, 51] 2596->2598 2599 age <= 29.5 entropy = 0.988 samples = 87 value = [38, 49] 2598->2599 2694 entropy = 0.0 samples = 2 value = [0, 2] 2598->2694 2600 race_White <= 0.5 entropy = 0.993 samples = 31 value = [17, 14] 2599->2600 2641 hours-per-week <= 72.5 entropy = 0.954 samples = 56 value = [21, 35] 2599->2641 2601 entropy = 0.0 samples = 2 value = [2, 0] 2600->2601 2602 education <= 15.5 entropy = 0.999 samples = 29 value = [15, 14] 2600->2602 2603 age <= 28.5 entropy = 0.996 samples = 28 value = [15, 13] 2602->2603 2640 entropy = 0.0 samples = 1 value = [0, 1] 2602->2640 2604 hours-per-week <= 65.0 entropy = 0.991 samples = 18 value = [8, 10] 2603->2604 2625 education <= 13.5 entropy = 0.881 samples = 10 value = [7, 3] 2603->2625 2605 education <= 12.5 entropy = 1.0 samples = 16 value = [8, 8] 2604->2605 2624 entropy = 0.0 samples = 2 value = [0, 2] 2604->2624 2606 entropy = 0.0 samples = 1 value = [1, 0] 2605->2606 2607 hours-per-week <= 57.5 entropy = 0.997 samples = 15 value = [7, 8] 2605->2607 2608 education <= 13.5 entropy = 0.994 samples = 11 value = [6, 5] 2607->2608 2621 workclass_Private <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 2607->2621 2609 sex_Male <= 0.5 entropy = 0.954 samples = 8 value = [5, 3] 2608->2609 2618 hours-per-week <= 52.5 entropy = 0.918 samples = 3 value = [1, 2] 2608->2618 2610 entropy = 0.0 samples = 1 value = [0, 1] 2609->2610 2611 hours-per-week <= 52.5 entropy = 0.863 samples = 7 value = [5, 2] 2609->2611 2612 age <= 26.5 entropy = 0.65 samples = 6 value = [5, 1] 2611->2612 2617 entropy = 0.0 samples = 1 value = [0, 1] 2611->2617 2613 age <= 25.5 entropy = 0.918 samples = 3 value = [2, 1] 2612->2613 2616 entropy = 0.0 samples = 3 value = [3, 0] 2612->2616 2614 entropy = 0.0 samples = 1 value = [1, 0] 2613->2614 2615 entropy = 1.0 samples = 2 value = [1, 1] 2613->2615 2619 entropy = 0.0 samples = 2 value = [0, 2] 2618->2619 2620 entropy = 0.0 samples = 1 value = [1, 0] 2618->2620 2622 entropy = 0.0 samples = 1 value = [1, 0] 2621->2622 2623 entropy = 0.0 samples = 3 value = [0, 3] 2621->2623 2626 hours-per-week <= 65.0 entropy = 0.954 samples = 8 value = [5, 3] 2625->2626 2639 entropy = 0.0 samples = 2 value = [2, 0] 2625->2639 2627 workclass_Public <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 2626->2627 2638 entropy = 0.0 samples = 1 value = [0, 1] 2626->2638 2628 workclass_Private <= 0.5 entropy = 0.65 samples = 6 value = [5, 1] 2627->2628 2637 entropy = 0.0 samples = 1 value = [0, 1] 2627->2637 2629 entropy = 0.0 samples = 1 value = [1, 0] 2628->2629 2630 education <= 12.5 entropy = 0.722 samples = 5 value = [4, 1] 2628->2630 2631 sex_Male <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 2630->2631 2636 entropy = 0.0 samples = 1 value = [1, 0] 2630->2636 2632 entropy = 0.0 samples = 1 value = [1, 0] 2631->2632 2633 hours-per-week <= 55.0 entropy = 0.918 samples = 3 value = [2, 1] 2631->2633 2634 entropy = 1.0 samples = 2 value = [1, 1] 2633->2634 2635 entropy = 0.0 samples = 1 value = [1, 0] 2633->2635 2642 hours-per-week <= 62.5 entropy = 0.964 samples = 54 value = [21, 33] 2641->2642 2693 entropy = 0.0 samples = 2 value = [0, 2] 2641->2693 2643 workclass_Public <= 0.5 entropy = 0.931 samples = 49 value = [17, 32] 2642->2643 2688 age <= 30.5 entropy = 0.722 samples = 5 value = [4, 1] 2642->2688 2644 age <= 31.5 entropy = 0.893 samples = 42 value = [13, 29] 2643->2644 2679 age <= 31.5 entropy = 0.985 samples = 7 value = [4, 3] 2643->2679 2645 education <= 13.5 entropy = 0.991 samples = 18 value = [8, 10] 2644->2645 2664 hours-per-week <= 57.5 entropy = 0.738 samples = 24 value = [5, 19] 2644->2664 2646 hours-per-week <= 53.5 entropy = 0.918 samples = 15 value = [5, 10] 2645->2646 2663 entropy = 0.0 samples = 3 value = [3, 0] 2645->2663 2647 education <= 12.5 entropy = 0.503 samples = 9 value = [1, 8] 2646->2647 2654 workclass_Private <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 2646->2654 2648 entropy = 0.0 samples = 3 value = [0, 3] 2647->2648 2649 age <= 30.5 entropy = 0.65 samples = 6 value = [1, 5] 2647->2649 2650 workclass_Private <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 2649->2650 2653 entropy = 0.0 samples = 2 value = [0, 2] 2649->2653 2651 entropy = 0.0 samples = 1 value = [0, 1] 2650->2651 2652 entropy = 0.918 samples = 3 value = [1, 2] 2650->2652 2655 entropy = 0.0 samples = 2 value = [2, 0] 2654->2655 2656 education <= 12.5 entropy = 1.0 samples = 4 value = [2, 2] 2654->2656 2657 entropy = 0.0 samples = 1 value = [1, 0] 2656->2657 2658 hours-per-week <= 57.5 entropy = 0.918 samples = 3 value = [1, 2] 2656->2658 2659 age <= 30.5 entropy = 1.0 samples = 2 value = [1, 1] 2658->2659 2662 entropy = 0.0 samples = 1 value = [0, 1] 2658->2662 2660 entropy = 0.0 samples = 1 value = [0, 1] 2659->2660 2661 entropy = 0.0 samples = 1 value = [1, 0] 2659->2661 2665 workclass_Self-emp <= 0.5 entropy = 0.896 samples = 16 value = [5, 11] 2664->2665 2678 entropy = 0.0 samples = 8 value = [0, 8] 2664->2678 2666 education <= 14.5 entropy = 0.961 samples = 13 value = [5, 8] 2665->2666 2677 entropy = 0.0 samples = 3 value = [0, 3] 2665->2677 2667 age <= 32.5 entropy = 0.994 samples = 11 value = [5, 6] 2666->2667 2676 entropy = 0.0 samples = 2 value = [0, 2] 2666->2676 2668 hours-per-week <= 52.5 entropy = 0.811 samples = 4 value = [1, 3] 2667->2668 2671 hours-per-week <= 52.5 entropy = 0.985 samples = 7 value = [4, 3] 2667->2671 2669 entropy = 0.918 samples = 3 value = [1, 2] 2668->2669 2670 entropy = 0.0 samples = 1 value = [0, 1] 2668->2670 2672 education <= 13.5 entropy = 0.971 samples = 5 value = [3, 2] 2671->2672 2675 entropy = 1.0 samples = 2 value = [1, 1] 2671->2675 2673 entropy = 0.918 samples = 3 value = [2, 1] 2672->2673 2674 entropy = 1.0 samples = 2 value = [1, 1] 2672->2674 2680 entropy = 0.0 samples = 2 value = [0, 2] 2679->2680 2681 age <= 32.5 entropy = 0.722 samples = 5 value = [4, 1] 2679->2681 2682 entropy = 0.0 samples = 2 value = [2, 0] 2681->2682 2683 hours-per-week <= 55.0 entropy = 0.918 samples = 3 value = [2, 1] 2681->2683 2684 education <= 13.5 entropy = 1.0 samples = 2 value = [1, 1] 2683->2684 2687 entropy = 0.0 samples = 1 value = [1, 0] 2683->2687 2685 entropy = 0.0 samples = 1 value = [1, 0] 2684->2685 2686 entropy = 0.0 samples = 1 value = [0, 1] 2684->2686 2689 entropy = 0.0 samples = 2 value = [2, 0] 2688->2689 2690 age <= 31.5 entropy = 0.918 samples = 3 value = [2, 1] 2688->2690 2691 entropy = 0.0 samples = 1 value = [0, 1] 2690->2691 2692 entropy = 0.0 samples = 2 value = [2, 0] 2690->2692 2696 race_White <= 0.5 entropy = 0.776 samples = 472 value = [108, 364] 2695->2696 3029 age <= 56.5 entropy = 0.337 samples = 112 value = [7, 105] 2695->3029 2697 workclass_Public <= 0.5 entropy = 0.99 samples = 25 value = [14, 11] 2696->2697 2722 hours-per-week <= 85.0 entropy = 0.742 samples = 447 value = [94, 353] 2696->2722 2698 workclass_Private <= 0.5 entropy = 0.946 samples = 22 value = [14, 8] 2697->2698 2721 entropy = 0.0 samples = 3 value = [0, 3] 2697->2721 2699 race_Hispanic <= 0.5 entropy = 0.544 samples = 8 value = [7, 1] 2698->2699 2704 age <= 42.5 entropy = 1.0 samples = 14 value = [7, 7] 2698->2704 2700 entropy = 0.0 samples = 6 value = [6, 0] 2699->2700 2701 hours-per-week <= 47.5 entropy = 1.0 samples = 2 value = [1, 1] 2699->2701 2702 entropy = 0.0 samples = 1 value = [1, 0] 2701->2702 2703 entropy = 0.0 samples = 1 value = [0, 1] 2701->2703 2705 hours-per-week <= 52.5 entropy = 0.863 samples = 7 value = [2, 5] 2704->2705 2714 race_Black <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 2704->2714 2706 age <= 37.0 entropy = 0.971 samples = 5 value = [2, 3] 2705->2706 2713 entropy = 0.0 samples = 2 value = [0, 2] 2705->2713 2707 entropy = 0.0 samples = 1 value = [1, 0] 2706->2707 2708 race_Black <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 2706->2708 2709 entropy = 0.0 samples = 2 value = [0, 2] 2708->2709 2710 sex_Male <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 2708->2710 2711 entropy = 0.0 samples = 1 value = [0, 1] 2710->2711 2712 entropy = 0.0 samples = 1 value = [1, 0] 2710->2712 2715 entropy = 0.0 samples = 4 value = [4, 0] 2714->2715 2716 age <= 46.5 entropy = 0.918 samples = 3 value = [1, 2] 2714->2716 2717 entropy = 0.0 samples = 1 value = [0, 1] 2716->2717 2718 hours-per-week <= 46.5 entropy = 1.0 samples = 2 value = [1, 1] 2716->2718 2719 entropy = 0.0 samples = 1 value = [0, 1] 2718->2719 2720 entropy = 0.0 samples = 1 value = [1, 0] 2718->2720 2723 age <= 41.5 entropy = 0.729 samples = 442 value = [90, 352] 2722->2723 3026 age <= 40.5 entropy = 0.722 samples = 5 value = [4, 1] 2722->3026 2724 age <= 40.5 entropy = 0.6 samples = 178 value = [26, 152] 2723->2724 2819 workclass_Private <= 0.5 entropy = 0.799 samples = 264 value = [64, 200] 2723->2819 2725 education <= 12.5 entropy = 0.635 samples = 162 value = [26, 136] 2724->2725 2818 entropy = 0.0 samples = 16 value = [0, 16] 2724->2818 2726 entropy = 0.0 samples = 12 value = [0, 12] 2725->2726 2727 hours-per-week <= 57.5 entropy = 0.665 samples = 150 value = [26, 124] 2725->2727 2728 workclass_Public <= 0.5 entropy = 0.592 samples = 112 value = [16, 96] 2727->2728 2787 education <= 13.5 entropy = 0.831 samples = 38 value = [10, 28] 2727->2787 2729 hours-per-week <= 49.0 entropy = 0.642 samples = 98 value = [16, 82] 2728->2729 2786 entropy = 0.0 samples = 14 value = [0, 14] 2728->2786 2730 hours-per-week <= 44.0 entropy = 0.811 samples = 36 value = [9, 27] 2729->2730 2759 age <= 38.5 entropy = 0.509 samples = 62 value = [7, 55] 2729->2759 2731 entropy = 0.0 samples = 3 value = [0, 3] 2730->2731 2732 age <= 35.5 entropy = 0.845 samples = 33 value = [9, 24] 2730->2732 2733 hours-per-week <= 47.5 entropy = 0.684 samples = 11 value = [2, 9] 2732->2733 2738 hours-per-week <= 45.5 entropy = 0.902 samples = 22 value = [7, 15] 2732->2738 2734 age <= 34.5 entropy = 0.469 samples = 10 value = [1, 9] 2733->2734 2737 entropy = 0.0 samples = 1 value = [1, 0] 2733->2737 2735 entropy = 0.811 samples = 4 value = [1, 3] 2734->2735 2736 entropy = 0.0 samples = 6 value = [0, 6] 2734->2736 2739 sex_Female <= 0.5 entropy = 0.949 samples = 19 value = [7, 12] 2738->2739 2758 entropy = 0.0 samples = 3 value = [0, 3] 2738->2758 2740 workclass_Private <= 0.5 entropy = 0.964 samples = 18 value = [7, 11] 2739->2740 2757 entropy = 0.0 samples = 1 value = [0, 1] 2739->2757 2741 age <= 39.5 entropy = 0.918 samples = 3 value = [2, 1] 2740->2741 2744 age <= 39.5 entropy = 0.918 samples = 15 value = [5, 10] 2740->2744 2742 entropy = 0.0 samples = 2 value = [2, 0] 2741->2742 2743 entropy = 0.0 samples = 1 value = [0, 1] 2741->2743 2745 age <= 38.5 entropy = 0.863 samples = 14 value = [4, 10] 2744->2745 2756 entropy = 0.0 samples = 1 value = [1, 0] 2744->2756 2746 education <= 13.5 entropy = 0.918 samples = 12 value = [4, 8] 2745->2746 2755 entropy = 0.0 samples = 2 value = [0, 2] 2745->2755 2747 age <= 36.5 entropy = 0.811 samples = 8 value = [2, 6] 2746->2747 2750 age <= 36.5 entropy = 1.0 samples = 4 value = [2, 2] 2746->2750 2748 entropy = 1.0 samples = 4 value = [2, 2] 2747->2748 2749 entropy = 0.0 samples = 4 value = [0, 4] 2747->2749 2751 entropy = 0.0 samples = 1 value = [0, 1] 2750->2751 2752 age <= 37.5 entropy = 0.918 samples = 3 value = [2, 1] 2750->2752 2753 entropy = 1.0 samples = 2 value = [1, 1] 2752->2753 2754 entropy = 0.0 samples = 1 value = [1, 0] 2752->2754 2760 age <= 34.5 entropy = 0.592 samples = 49 value = [7, 42] 2759->2760 2785 entropy = 0.0 samples = 13 value = [0, 13] 2759->2785 2761 entropy = 0.0 samples = 10 value = [0, 10] 2760->2761 2762 education <= 13.5 entropy = 0.679 samples = 39 value = [7, 32] 2760->2762 2763 workclass_Self-emp <= 0.5 entropy = 0.722 samples = 35 value = [7, 28] 2762->2763 2784 entropy = 0.0 samples = 4 value = [0, 4] 2762->2784 2764 age <= 37.5 entropy = 0.758 samples = 32 value = [7, 25] 2763->2764 2783 entropy = 0.0 samples = 3 value = [0, 3] 2763->2783 2765 hours-per-week <= 53.5 entropy = 0.845 samples = 22 value = [6, 16] 2764->2765 2780 hours-per-week <= 52.5 entropy = 0.469 samples = 10 value = [1, 9] 2764->2780 2766 hours-per-week <= 51.0 entropy = 0.764 samples = 18 value = [4, 14] 2765->2766 2775 age <= 36.5 entropy = 1.0 samples = 4 value = [2, 2] 2765->2775 2767 sex_Male <= 0.5 entropy = 0.811 samples = 16 value = [4, 12] 2766->2767 2774 entropy = 0.0 samples = 2 value = [0, 2] 2766->2774 2768 entropy = 0.0 samples = 1 value = [0, 1] 2767->2768 2769 age <= 35.5 entropy = 0.837 samples = 15 value = [4, 11] 2767->2769 2770 entropy = 0.918 samples = 3 value = [1, 2] 2769->2770 2771 age <= 36.5 entropy = 0.811 samples = 12 value = [3, 9] 2769->2771 2772 entropy = 0.722 samples = 5 value = [1, 4] 2771->2772 2773 entropy = 0.863 samples = 7 value = [2, 5] 2771->2773 2776 age <= 35.5 entropy = 0.918 samples = 3 value = [2, 1] 2775->2776 2779 entropy = 0.0 samples = 1 value = [0, 1] 2775->2779 2777 entropy = 1.0 samples = 2 value = [1, 1] 2776->2777 2778 entropy = 0.0 samples = 1 value = [1, 0] 2776->2778 2781 entropy = 0.544 samples = 8 value = [1, 7] 2780->2781 2782 entropy = 0.0 samples = 2 value = [0, 2] 2780->2782 2788 sex_Male <= 0.5 entropy = 0.94 samples = 28 value = [10, 18] 2787->2788 2817 entropy = 0.0 samples = 10 value = [0, 10] 2787->2817 2789 entropy = 0.0 samples = 1 value = [1, 0] 2788->2789 2790 hours-per-week <= 75.0 entropy = 0.918 samples = 27 value = [9, 18] 2788->2790 2791 hours-per-week <= 62.5 entropy = 0.943 samples = 25 value = [9, 16] 2790->2791 2816 entropy = 0.0 samples = 2 value = [0, 2] 2790->2816 2792 age <= 39.5 entropy = 0.863 samples = 21 value = [6, 15] 2791->2792 2811 age <= 34.5 entropy = 0.811 samples = 4 value = [3, 1] 2791->2811 2793 workclass_Public <= 0.5 entropy = 0.764 samples = 18 value = [4, 14] 2792->2793 2808 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 2792->2808 2794 age <= 34.5 entropy = 0.696 samples = 16 value = [3, 13] 2793->2794 2805 age <= 36.5 entropy = 1.0 samples = 2 value = [1, 1] 2793->2805 2795 entropy = 0.0 samples = 5 value = [0, 5] 2794->2795 2796 age <= 35.5 entropy = 0.845 samples = 11 value = [3, 8] 2794->2796 2797 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 2796->2797 2800 age <= 38.5 entropy = 0.592 samples = 7 value = [1, 6] 2796->2800 2798 entropy = 0.918 samples = 3 value = [1, 2] 2797->2798 2799 entropy = 0.0 samples = 1 value = [1, 0] 2797->2799 2801 entropy = 0.0 samples = 4 value = [0, 4] 2800->2801 2802 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 2800->2802 2803 entropy = 0.0 samples = 2 value = [0, 2] 2802->2803 2804 entropy = 0.0 samples = 1 value = [1, 0] 2802->2804 2806 entropy = 0.0 samples = 1 value = [1, 0] 2805->2806 2807 entropy = 0.0 samples = 1 value = [0, 1] 2805->2807 2809 entropy = 1.0 samples = 2 value = [1, 1] 2808->2809 2810 entropy = 0.0 samples = 1 value = [1, 0] 2808->2810 2812 entropy = 0.0 samples = 2 value = [2, 0] 2811->2812 2813 age <= 37.0 entropy = 1.0 samples = 2 value = [1, 1] 2811->2813 2814 entropy = 0.0 samples = 1 value = [0, 1] 2813->2814 2815 entropy = 0.0 samples = 1 value = [1, 0] 2813->2815 2820 education <= 12.5 entropy = 0.879 samples = 114 value = [34, 80] 2819->2820 2923 age <= 80.0 entropy = 0.722 samples = 150 value = [30, 120] 2819->2923 2821 entropy = 0.0 samples = 2 value = [2, 0] 2820->2821 2822 education <= 13.5 entropy = 0.863 samples = 112 value = [32, 80] 2820->2822 2823 hours-per-week <= 53.5 entropy = 0.909 samples = 71 value = [23, 48] 2822->2823 2896 age <= 43.5 entropy = 0.759 samples = 41 value = [9, 32] 2822->2896 2824 age <= 82.5 entropy = 0.811 samples = 44 value = [11, 33] 2823->2824 2869 workclass_Public <= 0.5 entropy = 0.991 samples = 27 value = [12, 15] 2823->2869 2825 age <= 65.5 entropy = 0.782 samples = 43 value = [10, 33] 2824->2825 2868 entropy = 0.0 samples = 1 value = [1, 0] 2824->2868 2826 age <= 61.5 entropy = 0.801 samples = 41 value = [10, 31] 2825->2826 2867 entropy = 0.0 samples = 2 value = [0, 2] 2825->2867 2827 sex_Female <= 0.5 entropy = 0.769 samples = 40 value = [9, 31] 2826->2827 2866 entropy = 0.0 samples = 1 value = [1, 0] 2826->2866 2828 age <= 60.5 entropy = 0.822 samples = 35 value = [9, 26] 2827->2828 2865 entropy = 0.0 samples = 5 value = [0, 5] 2827->2865 2829 age <= 58.0 entropy = 0.845 samples = 33 value = [9, 24] 2828->2829 2864 entropy = 0.0 samples = 2 value = [0, 2] 2828->2864 2830 age <= 51.5 entropy = 0.811 samples = 32 value = [8, 24] 2829->2830 2863 entropy = 0.0 samples = 1 value = [1, 0] 2829->2863 2831 hours-per-week <= 46.5 entropy = 0.877 samples = 27 value = [8, 19] 2830->2831 2862 entropy = 0.0 samples = 5 value = [0, 5] 2830->2862 2832 age <= 43.5 entropy = 0.971 samples = 10 value = [4, 6] 2831->2832 2845 age <= 42.5 entropy = 0.787 samples = 17 value = [4, 13] 2831->2845 2833 entropy = 0.0 samples = 1 value = [1, 0] 2832->2833 2834 age <= 50.5 entropy = 0.918 samples = 9 value = [3, 6] 2832->2834 2835 age <= 47.5 entropy = 0.811 samples = 8 value = [2, 6] 2834->2835 2844 entropy = 0.0 samples = 1 value = [1, 0] 2834->2844 2836 workclass_Public <= 0.5 entropy = 0.65 samples = 6 value = [1, 5] 2835->2836 2841 age <= 49.0 entropy = 1.0 samples = 2 value = [1, 1] 2835->2841 2837 hours-per-week <= 43.5 entropy = 0.918 samples = 3 value = [1, 2] 2836->2837 2840 entropy = 0.0 samples = 3 value = [0, 3] 2836->2840 2838 entropy = 0.0 samples = 1 value = [0, 1] 2837->2838 2839 entropy = 1.0 samples = 2 value = [1, 1] 2837->2839 2842 entropy = 0.0 samples = 1 value = [1, 0] 2841->2842 2843 entropy = 0.0 samples = 1 value = [0, 1] 2841->2843 2846 entropy = 0.0 samples = 4 value = [0, 4] 2845->2846 2847 age <= 43.5 entropy = 0.89 samples = 13 value = [4, 9] 2845->2847 2848 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 2847->2848 2851 age <= 45.5 entropy = 0.722 samples = 10 value = [2, 8] 2847->2851 2849 entropy = 1.0 samples = 2 value = [1, 1] 2848->2849 2850 entropy = 0.0 samples = 1 value = [1, 0] 2848->2850 2852 entropy = 0.0 samples = 4 value = [0, 4] 2851->2852 2853 age <= 46.5 entropy = 0.918 samples = 6 value = [2, 4] 2851->2853 2854 entropy = 0.0 samples = 1 value = [1, 0] 2853->2854 2855 age <= 48.5 entropy = 0.722 samples = 5 value = [1, 4] 2853->2855 2856 entropy = 0.0 samples = 2 value = [0, 2] 2855->2856 2857 age <= 50.0 entropy = 0.918 samples = 3 value = [1, 2] 2855->2857 2858 hours-per-week <= 51.0 entropy = 1.0 samples = 2 value = [1, 1] 2857->2858 2861 entropy = 0.0 samples = 1 value = [0, 1] 2857->2861 2859 entropy = 0.0 samples = 1 value = [1, 0] 2858->2859 2860 entropy = 0.0 samples = 1 value = [0, 1] 2858->2860 2870 age <= 59.0 entropy = 0.954 samples = 24 value = [9, 15] 2869->2870 2895 entropy = 0.0 samples = 3 value = [3, 0] 2869->2895 2871 age <= 46.5 entropy = 0.993 samples = 20 value = [9, 11] 2870->2871 2894 entropy = 0.0 samples = 4 value = [0, 4] 2870->2894 2872 age <= 42.5 entropy = 0.811 samples = 8 value = [2, 6] 2871->2872 2879 hours-per-week <= 57.5 entropy = 0.98 samples = 12 value = [7, 5] 2871->2879 2873 hours-per-week <= 75.0 entropy = 0.971 samples = 5 value = [2, 3] 2872->2873 2878 entropy = 0.0 samples = 3 value = [0, 3] 2872->2878 2874 hours-per-week <= 65.0 entropy = 1.0 samples = 4 value = [2, 2] 2873->2874 2877 entropy = 0.0 samples = 1 value = [0, 1] 2873->2877 2875 entropy = 1.0 samples = 2 value = [1, 1] 2874->2875 2876 entropy = 1.0 samples = 2 value = [1, 1] 2874->2876 2880 entropy = 0.0 samples = 1 value = [1, 0] 2879->2880 2881 age <= 55.5 entropy = 0.994 samples = 11 value = [6, 5] 2879->2881 2882 hours-per-week <= 67.5 entropy = 1.0 samples = 10 value = [5, 5] 2881->2882 2893 entropy = 0.0 samples = 1 value = [1, 0] 2881->2893 2883 age <= 53.5 entropy = 0.991 samples = 9 value = [4, 5] 2882->2883 2892 entropy = 0.0 samples = 1 value = [1, 0] 2882->2892 2884 hours-per-week <= 62.5 entropy = 1.0 samples = 8 value = [4, 4] 2883->2884 2891 entropy = 0.0 samples = 1 value = [0, 1] 2883->2891 2885 age <= 48.5 entropy = 0.985 samples = 7 value = [4, 3] 2884->2885 2890 entropy = 0.0 samples = 1 value = [0, 1] 2884->2890 2886 entropy = 0.918 samples = 3 value = [2, 1] 2885->2886 2887 age <= 51.0 entropy = 1.0 samples = 4 value = [2, 2] 2885->2887 2888 entropy = 1.0 samples = 2 value = [1, 1] 2887->2888 2889 entropy = 1.0 samples = 2 value = [1, 1] 2887->2889 2897 workclass_Public <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] 2896->2897 2900 age <= 49.5 entropy = 0.65 samples = 36 value = [6, 30] 2896->2900 2898 entropy = 0.0 samples = 3 value = [3, 0] 2897->2898 2899 entropy = 0.0 samples = 2 value = [0, 2] 2897->2899 2901 entropy = 0.0 samples = 14 value = [0, 14] 2900->2901 2902 sex_Female <= 0.5 entropy = 0.845 samples = 22 value = [6, 16] 2900->2902 2903 age <= 50.5 entropy = 0.722 samples = 20 value = [4, 16] 2902->2903 2922 entropy = 0.0 samples = 2 value = [2, 0] 2902->2922 2904 entropy = 0.0 samples = 1 value = [1, 0] 2903->2904 2905 age <= 57.5 entropy = 0.629 samples = 19 value = [3, 16] 2903->2905 2906 workclass_Self-emp <= 0.5 entropy = 0.779 samples = 13 value = [3, 10] 2905->2906 2921 entropy = 0.0 samples = 6 value = [0, 6] 2905->2921 2907 age <= 53.0 entropy = 0.918 samples = 6 value = [2, 4] 2906->2907 2914 age <= 54.5 entropy = 0.592 samples = 7 value = [1, 6] 2906->2914 2908 hours-per-week <= 52.5 entropy = 1.0 samples = 2 value = [1, 1] 2907->2908 2911 hours-per-week <= 55.0 entropy = 0.811 samples = 4 value = [1, 3] 2907->2911 2909 entropy = 0.0 samples = 1 value = [1, 0] 2908->2909 2910 entropy = 0.0 samples = 1 value = [0, 1] 2908->2910 2912 entropy = 0.0 samples = 3 value = [0, 3] 2911->2912 2913 entropy = 0.0 samples = 1 value = [1, 0] 2911->2913 2915 age <= 52.5 entropy = 0.811 samples = 4 value = [1, 3] 2914->2915 2920 entropy = 0.0 samples = 3 value = [0, 3] 2914->2920 2916 entropy = 0.0 samples = 2 value = [0, 2] 2915->2916 2917 hours-per-week <= 47.5 entropy = 1.0 samples = 2 value = [1, 1] 2915->2917 2918 entropy = 0.0 samples = 1 value = [0, 1] 2917->2918 2919 entropy = 0.0 samples = 1 value = [1, 0] 2917->2919 2924 age <= 50.5 entropy = 0.711 samples = 149 value = [29, 120] 2923->2924 3025 entropy = 0.0 samples = 1 value = [1, 0] 2923->3025 2925 hours-per-week <= 67.5 entropy = 0.764 samples = 99 value = [22, 77] 2924->2925 2996 hours-per-week <= 49.0 entropy = 0.584 samples = 50 value = [7, 43] 2924->2996 2926 education <= 12.5 entropy = 0.75 samples = 98 value = [21, 77] 2925->2926 2995 entropy = 0.0 samples = 1 value = [1, 0] 2925->2995 2927 age <= 46.5 entropy = 0.971 samples = 10 value = [4, 6] 2926->2927 2936 age <= 44.5 entropy = 0.708 samples = 88 value = [17, 71] 2926->2936 2928 entropy = 0.0 samples = 4 value = [0, 4] 2927->2928 2929 hours-per-week <= 47.5 entropy = 0.918 samples = 6 value = [4, 2] 2927->2929 2930 entropy = 0.0 samples = 2 value = [2, 0] 2929->2930 2931 age <= 49.5 entropy = 1.0 samples = 4 value = [2, 2] 2929->2931 2932 hours-per-week <= 55.0 entropy = 0.918 samples = 3 value = [1, 2] 2931->2932 2935 entropy = 0.0 samples = 1 value = [1, 0] 2931->2935 2933 entropy = 1.0 samples = 2 value = [1, 1] 2932->2933 2934 entropy = 0.0 samples = 1 value = [0, 1] 2932->2934 2937 education <= 13.5 entropy = 0.822 samples = 35 value = [9, 26] 2936->2937 2964 hours-per-week <= 44.5 entropy = 0.612 samples = 53 value = [8, 45] 2936->2964 2938 hours-per-week <= 62.5 entropy = 0.89 samples = 26 value = [8, 18] 2937->2938 2957 hours-per-week <= 47.5 entropy = 0.503 samples = 9 value = [1, 8] 2937->2957 2939 hours-per-week <= 56.5 entropy = 0.904 samples = 25 value = [8, 17] 2938->2939 2956 entropy = 0.0 samples = 1 value = [0, 1] 2938->2956 2940 age <= 42.5 entropy = 0.874 samples = 17 value = [5, 12] 2939->2940 2949 hours-per-week <= 59.0 entropy = 0.954 samples = 8 value = [3, 5] 2939->2949 2941 entropy = 0.0 samples = 4 value = [0, 4] 2940->2941 2942 hours-per-week <= 47.5 entropy = 0.961 samples = 13 value = [5, 8] 2940->2942 2943 age <= 43.5 entropy = 0.918 samples = 3 value = [2, 1] 2942->2943 2946 age <= 43.5 entropy = 0.881 samples = 10 value = [3, 7] 2942->2946 2944 entropy = 1.0 samples = 2 value = [1, 1] 2943->2944 2945 entropy = 0.0 samples = 1 value = [1, 0] 2943->2945 2947 entropy = 0.971 samples = 5 value = [2, 3] 2946->2947 2948 entropy = 0.722 samples = 5 value = [1, 4] 2946->2948 2950 entropy = 0.0 samples = 1 value = [1, 0] 2949->2950 2951 age <= 42.5 entropy = 0.863 samples = 7 value = [2, 5] 2949->2951 2952 entropy = 1.0 samples = 2 value = [1, 1] 2951->2952 2953 age <= 43.5 entropy = 0.722 samples = 5 value = [1, 4] 2951->2953 2954 entropy = 0.0 samples = 1 value = [0, 1] 2953->2954 2955 entropy = 0.811 samples = 4 value = [1, 3] 2953->2955 2958 entropy = 0.0 samples = 4 value = [0, 4] 2957->2958 2959 age <= 42.5 entropy = 0.722 samples = 5 value = [1, 4] 2957->2959 2960 entropy = 0.0 samples = 2 value = [0, 2] 2959->2960 2961 age <= 43.5 entropy = 0.918 samples = 3 value = [1, 2] 2959->2961 2962 entropy = 1.0 samples = 2 value = [1, 1] 2961->2962 2963 entropy = 0.0 samples = 1 value = [0, 1] 2961->2963 2965 education <= 13.5 entropy = 1.0 samples = 4 value = [2, 2] 2964->2965 2970 hours-per-week <= 53.5 entropy = 0.536 samples = 49 value = [6, 43] 2964->2970 2966 age <= 47.5 entropy = 0.918 samples = 3 value = [1, 2] 2965->2966 2969 entropy = 0.0 samples = 1 value = [1, 0] 2965->2969 2967 entropy = 0.0 samples = 2 value = [0, 2] 2966->2967 2968 entropy = 0.0 samples = 1 value = [1, 0] 2966->2968 2971 age <= 47.5 entropy = 0.345 samples = 31 value = [2, 29] 2970->2971 2982 age <= 49.5 entropy = 0.764 samples = 18 value = [4, 14] 2970->2982 2972 entropy = 0.0 samples = 13 value = [0, 13] 2971->2972 2973 education <= 13.5 entropy = 0.503 samples = 18 value = [2, 16] 2971->2973 2974 hours-per-week <= 47.5 entropy = 0.619 samples = 13 value = [2, 11] 2973->2974 2981 entropy = 0.0 samples = 5 value = [0, 5] 2973->2981 2975 age <= 48.5 entropy = 0.503 samples = 9 value = [1, 8] 2974->2975 2978 age <= 48.5 entropy = 0.811 samples = 4 value = [1, 3] 2974->2978 2976 entropy = 0.0 samples = 6 value = [0, 6] 2975->2976 2977 entropy = 0.918 samples = 3 value = [1, 2] 2975->2977 2979 entropy = 1.0 samples = 2 value = [1, 1] 2978->2979 2980 entropy = 0.0 samples = 2 value = [0, 2] 2978->2980 2983 age <= 46.5 entropy = 0.696 samples = 16 value = [3, 13] 2982->2983 2994 entropy = 1.0 samples = 2 value = [1, 1] 2982->2994 2984 education <= 13.5 entropy = 0.845 samples = 11 value = [3, 8] 2983->2984 2993 entropy = 0.0 samples = 5 value = [0, 5] 2983->2993 2985 hours-per-week <= 57.5 entropy = 0.811 samples = 8 value = [2, 6] 2984->2985 2990 hours-per-week <= 57.5 entropy = 0.918 samples = 3 value = [1, 2] 2984->2990 2986 age <= 45.5 entropy = 0.971 samples = 5 value = [2, 3] 2985->2986 2989 entropy = 0.0 samples = 3 value = [0, 3] 2985->2989 2987 entropy = 1.0 samples = 4 value = [2, 2] 2986->2987 2988 entropy = 0.0 samples = 1 value = [0, 1] 2986->2988 2991 entropy = 0.0 samples = 2 value = [0, 2] 2990->2991 2992 entropy = 0.0 samples = 1 value = [1, 0] 2990->2992 2997 entropy = 0.0 samples = 11 value = [0, 11] 2996->2997 2998 hours-per-week <= 62.5 entropy = 0.679 samples = 39 value = [7, 32] 2996->2998 2999 age <= 55.5 entropy = 0.722 samples = 35 value = [7, 28] 2998->2999 3024 entropy = 0.0 samples = 4 value = [0, 4] 2998->3024 3000 age <= 54.5 entropy = 0.559 samples = 23 value = [3, 20] 2999->3000 3013 education <= 13.5 entropy = 0.918 samples = 12 value = [4, 8] 2999->3013 3001 age <= 53.5 entropy = 0.65 samples = 18 value = [3, 15] 3000->3001 3012 entropy = 0.0 samples = 5 value = [0, 5] 3000->3012 3002 hours-per-week <= 52.5 entropy = 0.523 samples = 17 value = [2, 15] 3001->3002 3011 entropy = 0.0 samples = 1 value = [1, 0] 3001->3011 3003 education <= 13.5 entropy = 0.764 samples = 9 value = [2, 7] 3002->3003 3010 entropy = 0.0 samples = 8 value = [0, 8] 3002->3010 3004 age <= 52.5 entropy = 0.918 samples = 6 value = [2, 4] 3003->3004 3009 entropy = 0.0 samples = 3 value = [0, 3] 3003->3009 3005 age <= 51.5 entropy = 0.971 samples = 5 value = [2, 3] 3004->3005 3008 entropy = 0.0 samples = 1 value = [0, 1] 3004->3008 3006 entropy = 0.918 samples = 3 value = [1, 2] 3005->3006 3007 entropy = 1.0 samples = 2 value = [1, 1] 3005->3007 3014 age <= 57.5 entropy = 0.65 samples = 6 value = [1, 5] 3013->3014 3017 hours-per-week <= 57.5 entropy = 1.0 samples = 6 value = [3, 3] 3013->3017 3015 entropy = 1.0 samples = 2 value = [1, 1] 3014->3015 3016 entropy = 0.0 samples = 4 value = [0, 4] 3014->3016 3018 entropy = 0.0 samples = 2 value = [2, 0] 3017->3018 3019 age <= 60.0 entropy = 0.811 samples = 4 value = [1, 3] 3017->3019 3020 age <= 58.0 entropy = 1.0 samples = 2 value = [1, 1] 3019->3020 3023 entropy = 0.0 samples = 2 value = [0, 2] 3019->3023 3021 entropy = 0.0 samples = 1 value = [0, 1] 3020->3021 3022 entropy = 0.0 samples = 1 value = [1, 0] 3020->3022 3027 entropy = 0.0 samples = 1 value = [0, 1] 3026->3027 3028 entropy = 0.0 samples = 4 value = [4, 0] 3026->3028 3030 age <= 38.5 entropy = 0.248 samples = 97 value = [4, 93] 3029->3030 3055 education <= 15.5 entropy = 0.722 samples = 15 value = [3, 12] 3029->3055 3031 age <= 35.5 entropy = 0.61 samples = 20 value = [3, 17] 3030->3031 3046 education <= 15.5 entropy = 0.1 samples = 77 value = [1, 76] 3030->3046 3032 entropy = 0.0 samples = 7 value = [0, 7] 3031->3032 3033 age <= 36.5 entropy = 0.779 samples = 13 value = [3, 10] 3031->3033 3034 entropy = 0.0 samples = 1 value = [1, 0] 3033->3034 3035 hours-per-week <= 65.0 entropy = 0.65 samples = 12 value = [2, 10] 3033->3035 3036 hours-per-week <= 55.0 entropy = 0.811 samples = 8 value = [2, 6] 3035->3036 3045 entropy = 0.0 samples = 4 value = [0, 4] 3035->3045 3037 workclass_Self-emp <= 0.5 entropy = 0.65 samples = 6 value = [1, 5] 3036->3037 3044 entropy = 1.0 samples = 2 value = [1, 1] 3036->3044 3038 entropy = 0.0 samples = 2 value = [0, 2] 3037->3038 3039 age <= 37.5 entropy = 0.811 samples = 4 value = [1, 3] 3037->3039 3040 entropy = 0.0 samples = 1 value = [0, 1] 3039->3040 3041 hours-per-week <= 47.5 entropy = 0.918 samples = 3 value = [1, 2] 3039->3041 3042 entropy = 0.0 samples = 1 value = [0, 1] 3041->3042 3043 entropy = 1.0 samples = 2 value = [1, 1] 3041->3043 3047 entropy = 0.0 samples = 48 value = [0, 48] 3046->3047 3048 workclass_Self-emp <= 0.5 entropy = 0.216 samples = 29 value = [1, 28] 3046->3048 3049 entropy = 0.0 samples = 22 value = [0, 22] 3048->3049 3050 hours-per-week <= 55.0 entropy = 0.592 samples = 7 value = [1, 6] 3048->3050 3051 age <= 50.5 entropy = 1.0 samples = 2 value = [1, 1] 3050->3051 3054 entropy = 0.0 samples = 5 value = [0, 5] 3050->3054 3052 entropy = 0.0 samples = 1 value = [1, 0] 3051->3052 3053 entropy = 0.0 samples = 1 value = [0, 1] 3051->3053 3056 age <= 66.5 entropy = 0.881 samples = 10 value = [3, 7] 3055->3056 3063 entropy = 0.0 samples = 5 value = [0, 5] 3055->3063 3057 workclass_Private <= 0.5 entropy = 0.954 samples = 8 value = [3, 5] 3056->3057 3062 entropy = 0.0 samples = 2 value = [0, 2] 3056->3062 3058 age <= 64.5 entropy = 0.65 samples = 6 value = [1, 5] 3057->3058 3061 entropy = 0.0 samples = 2 value = [2, 0] 3057->3061 3059 entropy = 0.0 samples = 5 value = [0, 5] 3058->3059 3060 entropy = 0.0 samples = 1 value = [1, 0] 3058->3060 3065 age <= 31.5 entropy = 0.189 samples = 3675 value = [3569, 106] 3064->3065 3632 age <= 29.5 entropy = 0.734 samples = 1000 value = [794, 206] 3064->3632 3066 hours-per-week <= 40.5 entropy = 0.067 samples = 1994 value = [1978, 16] 3065->3066 3181 hours-per-week <= 42.5 entropy = 0.301 samples = 1681 value = [1591, 90] 3065->3181 3067 age <= 21.5 entropy = 0.039 samples = 1675 value = [1668, 7] 3066->3067 3114 hours-per-week <= 41.5 entropy = 0.185 samples = 319 value = [310, 9] 3066->3114 3068 entropy = 0.0 samples = 692 value = [692, 0] 3067->3068 3069 sex_Female <= 0.5 entropy = 0.061 samples = 983 value = [976, 7] 3067->3069 3070 education <= 9.5 entropy = 0.09 samples = 527 value = [521, 6] 3069->3070 3109 workclass_Self-emp <= 0.5 entropy = 0.023 samples = 456 value = [455, 1] 3069->3109 3071 race_Asian <= 0.5 entropy = 0.031 samples = 316 value = [315, 1] 3070->3071 3078 hours-per-week <= 27.5 entropy = 0.162 samples = 211 value = [206, 5] 3070->3078 3072 entropy = 0.0 samples = 307 value = [307, 0] 3071->3072 3073 education <= 8.0 entropy = 0.503 samples = 9 value = [8, 1] 3071->3073 3074 hours-per-week <= 35.0 entropy = 1.0 samples = 2 value = [1, 1] 3073->3074 3077 entropy = 0.0 samples = 7 value = [7, 0] 3073->3077 3075 entropy = 0.0 samples = 1 value = [1, 0] 3074->3075 3076 entropy = 0.0 samples = 1 value = [0, 1] 3074->3076 3079 entropy = 0.0 samples = 43 value = [43, 0] 3078->3079 3080 race_White <= 0.5 entropy = 0.193 samples = 168 value = [163, 5] 3078->3080 3081 entropy = 0.0 samples = 36 value = [36, 0] 3080->3081 3082 age <= 22.5 entropy = 0.232 samples = 132 value = [127, 5] 3080->3082 3083 entropy = 0.0 samples = 22 value = [22, 0] 3082->3083 3084 workclass_Private <= 0.5 entropy = 0.267 samples = 110 value = [105, 5] 3082->3084 3085 entropy = 0.0 samples = 16 value = [16, 0] 3084->3085 3086 education <= 11.5 entropy = 0.3 samples = 94 value = [89, 5] 3084->3086 3087 education <= 10.5 entropy = 0.331 samples = 82 value = [77, 5] 3086->3087 3108 entropy = 0.0 samples = 12 value = [12, 0] 3086->3108 3088 age <= 27.5 entropy = 0.258 samples = 69 value = [66, 3] 3087->3088 3101 hours-per-week <= 32.5 entropy = 0.619 samples = 13 value = [11, 2] 3087->3101 3089 age <= 23.5 entropy = 0.137 samples = 52 value = [51, 1] 3088->3089 3094 age <= 28.5 entropy = 0.523 samples = 17 value = [15, 2] 3088->3094 3090 hours-per-week <= 38.5 entropy = 0.297 samples = 19 value = [18, 1] 3089->3090 3093 entropy = 0.0 samples = 33 value = [33, 0] 3089->3093 3091 entropy = 0.0 samples = 6 value = [6, 0] 3090->3091 3092 entropy = 0.391 samples = 13 value = [12, 1] 3090->3092 3095 entropy = 0.811 samples = 4 value = [3, 1] 3094->3095 3096 age <= 29.5 entropy = 0.391 samples = 13 value = [12, 1] 3094->3096 3097 entropy = 0.0 samples = 3 value = [3, 0] 3096->3097 3098 age <= 30.5 entropy = 0.469 samples = 10 value = [9, 1] 3096->3098 3099 entropy = 0.544 samples = 8 value = [7, 1] 3098->3099 3100 entropy = 0.0 samples = 2 value = [2, 0] 3098->3100 3102 entropy = 0.0 samples = 1 value = [0, 1] 3101->3102 3103 age <= 25.5 entropy = 0.414 samples = 12 value = [11, 1] 3101->3103 3104 age <= 24.5 entropy = 0.722 samples = 5 value = [4, 1] 3103->3104 3107 entropy = 0.0 samples = 7 value = [7, 0] 3103->3107 3105 entropy = 0.0 samples = 3 value = [3, 0] 3104->3105 3106 entropy = 1.0 samples = 2 value = [1, 1] 3104->3106 3110 entropy = 0.0 samples = 450 value = [450, 0] 3109->3110 3111 age <= 24.0 entropy = 0.65 samples = 6 value = [5, 1] 3109->3111 3112 entropy = 0.0 samples = 1 value = [0, 1] 3111->3112 3113 entropy = 0.0 samples = 5 value = [5, 0] 3111->3113 3115 education <= 9.5 entropy = 0.722 samples = 5 value = [4, 1] 3114->3115 3120 age <= 29.5 entropy = 0.171 samples = 314 value = [306, 8] 3114->3120 3116 entropy = 0.0 samples = 3 value = [3, 0] 3115->3116 3117 education <= 11.0 entropy = 1.0 samples = 2 value = [1, 1] 3115->3117 3118 entropy = 0.0 samples = 1 value = [0, 1] 3117->3118 3119 entropy = 0.0 samples = 1 value = [1, 0] 3117->3119 3121 education <= 9.5 entropy = 0.136 samples = 263 value = [258, 5] 3120->3121 3160 sex_Male <= 0.5 entropy = 0.323 samples = 51 value = [48, 3] 3120->3160 3122 workclass_Private <= 0.5 entropy = 0.225 samples = 138 value = [133, 5] 3121->3122 3159 entropy = 0.0 samples = 125 value = [125, 0] 3121->3159 3123 age <= 25.0 entropy = 0.503 samples = 18 value = [16, 2] 3122->3123 3134 education <= 4.5 entropy = 0.169 samples = 120 value = [117, 3] 3122->3134 3124 race_Black <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] 3123->3124 3133 entropy = 0.0 samples = 9 value = [9, 0] 3123->3133 3125 workclass_Self-emp <= 0.5 entropy = 0.544 samples = 8 value = [7, 1] 3124->3125 3132 entropy = 0.0 samples = 1 value = [0, 1] 3124->3132 3126 race_White <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3125->3126 3131 entropy = 0.0 samples = 5 value = [5, 0] 3125->3131 3127 entropy = 0.0 samples = 1 value = [1, 0] 3126->3127 3128 education <= 8.5 entropy = 1.0 samples = 2 value = [1, 1] 3126->3128 3129 entropy = 0.0 samples = 1 value = [0, 1] 3128->3129 3130 entropy = 0.0 samples = 1 value = [1, 0] 3128->3130 3135 hours-per-week <= 55.0 entropy = 0.439 samples = 11 value = [10, 1] 3134->3135 3140 age <= 23.5 entropy = 0.132 samples = 109 value = [107, 2] 3134->3140 3136 entropy = 0.0 samples = 8 value = [8, 0] 3135->3136 3137 age <= 19.5 entropy = 0.918 samples = 3 value = [2, 1] 3135->3137 3138 entropy = 0.0 samples = 1 value = [0, 1] 3137->3138 3139 entropy = 0.0 samples = 2 value = [2, 0] 3137->3139 3141 entropy = 0.0 samples = 49 value = [49, 0] 3140->3141 3142 age <= 24.5 entropy = 0.211 samples = 60 value = [58, 2] 3140->3142 3143 hours-per-week <= 47.0 entropy = 0.544 samples = 8 value = [7, 1] 3142->3143 3148 age <= 26.5 entropy = 0.137 samples = 52 value = [51, 1] 3142->3148 3144 sex_Male <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3143->3144 3147 entropy = 0.0 samples = 5 value = [5, 0] 3143->3147 3145 entropy = 0.0 samples = 1 value = [0, 1] 3144->3145 3146 entropy = 0.0 samples = 2 value = [2, 0] 3144->3146 3149 entropy = 0.0 samples = 24 value = [24, 0] 3148->3149 3150 age <= 27.5 entropy = 0.222 samples = 28 value = [27, 1] 3148->3150 3151 sex_Female <= 0.5 entropy = 0.414 samples = 12 value = [11, 1] 3150->3151 3158 entropy = 0.0 samples = 16 value = [16, 0] 3150->3158 3152 education <= 8.5 entropy = 0.65 samples = 6 value = [5, 1] 3151->3152 3157 entropy = 0.0 samples = 6 value = [6, 0] 3151->3157 3153 entropy = 0.0 samples = 3 value = [3, 0] 3152->3153 3154 hours-per-week <= 49.0 entropy = 0.918 samples = 3 value = [2, 1] 3152->3154 3155 entropy = 0.0 samples = 1 value = [1, 0] 3154->3155 3156 entropy = 1.0 samples = 2 value = [1, 1] 3154->3156 3161 entropy = 0.0 samples = 14 value = [14, 0] 3160->3161 3162 workclass_Self-emp <= 0.5 entropy = 0.406 samples = 37 value = [34, 3] 3160->3162 3163 age <= 30.5 entropy = 0.323 samples = 34 value = [32, 2] 3162->3163 3178 age <= 30.5 entropy = 0.918 samples = 3 value = [2, 1] 3162->3178 3164 hours-per-week <= 51.0 entropy = 0.485 samples = 19 value = [17, 2] 3163->3164 3177 entropy = 0.0 samples = 15 value = [15, 0] 3163->3177 3165 hours-per-week <= 44.5 entropy = 0.619 samples = 13 value = [11, 2] 3164->3165 3176 entropy = 0.0 samples = 6 value = [6, 0] 3164->3176 3166 entropy = 0.0 samples = 3 value = [3, 0] 3165->3166 3167 education <= 7.5 entropy = 0.722 samples = 10 value = [8, 2] 3165->3167 3168 entropy = 0.0 samples = 2 value = [2, 0] 3167->3168 3169 education <= 9.5 entropy = 0.811 samples = 8 value = [6, 2] 3167->3169 3170 hours-per-week <= 47.5 entropy = 1.0 samples = 2 value = [1, 1] 3169->3170 3173 hours-per-week <= 49.0 entropy = 0.65 samples = 6 value = [5, 1] 3169->3173 3171 entropy = 0.0 samples = 1 value = [0, 1] 3170->3171 3172 entropy = 0.0 samples = 1 value = [1, 0] 3170->3172 3174 entropy = 0.0 samples = 3 value = [3, 0] 3173->3174 3175 entropy = 0.918 samples = 3 value = [2, 1] 3173->3175 3179 entropy = 0.0 samples = 2 value = [2, 0] 3178->3179 3180 entropy = 0.0 samples = 1 value = [0, 1] 3178->3180 3182 hours-per-week <= 35.5 entropy = 0.227 samples = 1279 value = [1232, 47] 3181->3182 3441 sex_Male <= 0.5 entropy = 0.491 samples = 402 value = [359, 43] 3181->3441 3183 workclass_Self-emp <= 0.5 entropy = 0.062 samples = 273 value = [271, 2] 3182->3183 3190 race_Black <= 0.5 entropy = 0.264 samples = 1006 value = [961, 45] 3182->3190 3184 entropy = 0.0 samples = 242 value = [242, 0] 3183->3184 3185 age <= 53.0 entropy = 0.345 samples = 31 value = [29, 2] 3183->3185 3186 entropy = 0.0 samples = 21 value = [21, 0] 3185->3186 3187 age <= 55.5 entropy = 0.722 samples = 10 value = [8, 2] 3185->3187 3188 entropy = 0.0 samples = 2 value = [0, 2] 3187->3188 3189 entropy = 0.0 samples = 8 value = [8, 0] 3187->3189 3191 sex_Male <= 0.5 entropy = 0.297 samples = 817 value = [774, 43] 3190->3191 3424 education <= 10.5 entropy = 0.085 samples = 189 value = [187, 2] 3190->3424 3192 age <= 34.5 entropy = 0.225 samples = 468 value = [451, 17] 3191->3192 3295 age <= 50.5 entropy = 0.382 samples = 349 value = [323, 26] 3191->3295 3193 entropy = 0.0 samples = 60 value = [60, 0] 3192->3193 3194 age <= 61.5 entropy = 0.25 samples = 408 value = [391, 17] 3192->3194 3195 race_White <= 0.5 entropy = 0.224 samples = 387 value = [373, 14] 3194->3195 3284 education <= 9.5 entropy = 0.592 samples = 21 value = [18, 3] 3194->3284 3196 education <= 9.5 entropy = 0.516 samples = 26 value = [23, 3] 3195->3196 3213 hours-per-week <= 37.5 entropy = 0.197 samples = 361 value = [350, 11] 3195->3213 3197 entropy = 0.0 samples = 10 value = [10, 0] 3196->3197 3198 age <= 45.5 entropy = 0.696 samples = 16 value = [13, 3] 3196->3198 3199 workclass_Public <= 0.5 entropy = 0.779 samples = 13 value = [10, 3] 3198->3199 3212 entropy = 0.0 samples = 3 value = [3, 0] 3198->3212 3200 race_Amer-Indian <= 0.5 entropy = 0.544 samples = 8 value = [7, 1] 3199->3200 3205 education <= 11.5 entropy = 0.971 samples = 5 value = [3, 2] 3199->3205 3201 age <= 38.0 entropy = 0.918 samples = 3 value = [2, 1] 3200->3201 3204 entropy = 0.0 samples = 5 value = [5, 0] 3200->3204 3202 entropy = 0.0 samples = 1 value = [0, 1] 3201->3202 3203 entropy = 0.0 samples = 2 value = [2, 0] 3201->3203 3206 age <= 36.5 entropy = 0.811 samples = 4 value = [3, 1] 3205->3206 3211 entropy = 0.0 samples = 1 value = [0, 1] 3205->3211 3207 entropy = 0.0 samples = 1 value = [1, 0] 3206->3207 3208 age <= 40.0 entropy = 0.918 samples = 3 value = [2, 1] 3206->3208 3209 entropy = 1.0 samples = 2 value = [1, 1] 3208->3209 3210 entropy = 0.0 samples = 1 value = [1, 0] 3208->3210 3214 age <= 40.5 entropy = 0.523 samples = 17 value = [15, 2] 3213->3214 3227 education <= 10.5 entropy = 0.175 samples = 344 value = [335, 9] 3213->3227 3215 entropy = 0.0 samples = 6 value = [6, 0] 3214->3215 3216 age <= 50.0 entropy = 0.684 samples = 11 value = [9, 2] 3214->3216 3217 education <= 10.5 entropy = 0.863 samples = 7 value = [5, 2] 3216->3217 3226 entropy = 0.0 samples = 4 value = [4, 0] 3216->3226 3218 age <= 44.0 entropy = 0.918 samples = 6 value = [4, 2] 3217->3218 3225 entropy = 0.0 samples = 1 value = [1, 0] 3217->3225 3219 education <= 9.5 entropy = 1.0 samples = 2 value = [1, 1] 3218->3219 3222 age <= 47.5 entropy = 0.811 samples = 4 value = [3, 1] 3218->3222 3220 entropy = 0.0 samples = 1 value = [1, 0] 3219->3220 3221 entropy = 0.0 samples = 1 value = [0, 1] 3219->3221 3223 entropy = 0.0 samples = 2 value = [2, 0] 3222->3223 3224 entropy = 1.0 samples = 2 value = [1, 1] 3222->3224 3228 age <= 54.5 entropy = 0.141 samples = 300 value = [294, 6] 3227->3228 3267 age <= 35.5 entropy = 0.359 samples = 44 value = [41, 3] 3227->3267 3229 age <= 44.5 entropy = 0.094 samples = 250 value = [247, 3] 3228->3229 3248 age <= 58.5 entropy = 0.327 samples = 50 value = [47, 3] 3228->3248 3230 age <= 43.5 entropy = 0.143 samples = 148 value = [145, 3] 3229->3230 3247 entropy = 0.0 samples = 102 value = [102, 0] 3229->3247 3231 age <= 39.5 entropy = 0.107 samples = 141 value = [139, 2] 3230->3231 3244 education <= 9.5 entropy = 0.592 samples = 7 value = [6, 1] 3230->3244 3232 age <= 37.5 entropy = 0.172 samples = 78 value = [76, 2] 3231->3232 3243 entropy = 0.0 samples = 63 value = [63, 0] 3231->3243 3233 entropy = 0.0 samples = 48 value = [48, 0] 3232->3233 3234 education <= 7.5 entropy = 0.353 samples = 30 value = [28, 2] 3232->3234 3235 education <= 5.5 entropy = 0.811 samples = 4 value = [3, 1] 3234->3235 3238 education <= 9.5 entropy = 0.235 samples = 26 value = [25, 1] 3234->3238 3236 entropy = 0.0 samples = 2 value = [2, 0] 3235->3236 3237 entropy = 1.0 samples = 2 value = [1, 1] 3235->3237 3239 entropy = 0.0 samples = 16 value = [16, 0] 3238->3239 3240 age <= 38.5 entropy = 0.469 samples = 10 value = [9, 1] 3238->3240 3241 entropy = 0.0 samples = 5 value = [5, 0] 3240->3241 3242 entropy = 0.722 samples = 5 value = [4, 1] 3240->3242 3245 entropy = 0.722 samples = 5 value = [4, 1] 3244->3245 3246 entropy = 0.0 samples = 2 value = [2, 0] 3244->3246 3249 education <= 8.0 entropy = 0.439 samples = 33 value = [30, 3] 3248->3249 3266 entropy = 0.0 samples = 17 value = [17, 0] 3248->3266 3250 entropy = 0.0 samples = 6 value = [6, 0] 3249->3250 3251 education <= 9.5 entropy = 0.503 samples = 27 value = [24, 3] 3249->3251 3252 age <= 56.5 entropy = 0.575 samples = 22 value = [19, 3] 3251->3252 3265 entropy = 0.0 samples = 5 value = [5, 0] 3251->3265 3253 workclass_Public <= 0.5 entropy = 0.391 samples = 13 value = [12, 1] 3252->3253 3260 workclass_Public <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] 3252->3260 3254 entropy = 0.0 samples = 10 value = [10, 0] 3253->3254 3255 age <= 55.5 entropy = 0.918 samples = 3 value = [2, 1] 3253->3255 3256 hours-per-week <= 39.0 entropy = 1.0 samples = 2 value = [1, 1] 3255->3256 3259 entropy = 0.0 samples = 1 value = [1, 0] 3255->3259 3257 entropy = 0.0 samples = 1 value = [1, 0] 3256->3257 3258 entropy = 0.0 samples = 1 value = [0, 1] 3256->3258 3261 age <= 57.5 entropy = 0.863 samples = 7 value = [5, 2] 3260->3261 3264 entropy = 0.0 samples = 2 value = [2, 0] 3260->3264 3262 entropy = 0.811 samples = 4 value = [3, 1] 3261->3262 3263 entropy = 0.918 samples = 3 value = [2, 1] 3261->3263 3268 education <= 11.5 entropy = 1.0 samples = 2 value = [1, 1] 3267->3268 3271 age <= 41.5 entropy = 0.276 samples = 42 value = [40, 2] 3267->3271 3269 entropy = 0.0 samples = 1 value = [0, 1] 3268->3269 3270 entropy = 0.0 samples = 1 value = [1, 0] 3268->3270 3272 entropy = 0.0 samples = 19 value = [19, 0] 3271->3272 3273 age <= 45.5 entropy = 0.426 samples = 23 value = [21, 2] 3271->3273 3274 workclass_Public <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 3273->3274 3283 entropy = 0.0 samples = 16 value = [16, 0] 3273->3283 3275 hours-per-week <= 39.0 entropy = 0.918 samples = 6 value = [4, 2] 3274->3275 3282 entropy = 0.0 samples = 1 value = [1, 0] 3274->3282 3276 entropy = 0.0 samples = 1 value = [1, 0] 3275->3276 3277 age <= 44.0 entropy = 0.971 samples = 5 value = [3, 2] 3275->3277 3278 education <= 11.5 entropy = 0.811 samples = 4 value = [3, 1] 3277->3278 3281 entropy = 0.0 samples = 1 value = [0, 1] 3277->3281 3279 entropy = 0.0 samples = 2 value = [2, 0] 3278->3279 3280 entropy = 1.0 samples = 2 value = [1, 1] 3278->3280 3285 entropy = 0.0 samples = 10 value = [10, 0] 3284->3285 3286 age <= 66.5 entropy = 0.845 samples = 11 value = [8, 3] 3284->3286 3287 age <= 65.5 entropy = 0.985 samples = 7 value = [4, 3] 3286->3287 3294 entropy = 0.0 samples = 4 value = [4, 0] 3286->3294 3288 education <= 11.0 entropy = 0.918 samples = 6 value = [4, 2] 3287->3288 3293 entropy = 0.0 samples = 1 value = [0, 1] 3287->3293 3289 hours-per-week <= 39.5 entropy = 0.722 samples = 5 value = [4, 1] 3288->3289 3292 entropy = 0.0 samples = 1 value = [0, 1] 3288->3292 3290 entropy = 0.0 samples = 1 value = [0, 1] 3289->3290 3291 entropy = 0.0 samples = 4 value = [4, 0] 3289->3291 3296 education <= 10.5 entropy = 0.328 samples = 283 value = [266, 17] 3295->3296 3379 education <= 11.5 entropy = 0.575 samples = 66 value = [57, 9] 3295->3379 3297 workclass_Public <= 0.5 entropy = 0.296 samples = 249 value = [236, 13] 3296->3297 3364 workclass_Public <= 0.5 entropy = 0.523 samples = 34 value = [30, 4] 3296->3364 3298 education <= 5.5 entropy = 0.323 samples = 204 value = [192, 12] 3297->3298 3357 age <= 33.5 entropy = 0.154 samples = 45 value = [44, 1] 3297->3357 3299 entropy = 0.0 samples = 8 value = [8, 0] 3298->3299 3300 hours-per-week <= 39.0 entropy = 0.332 samples = 196 value = [184, 12] 3298->3300 3301 entropy = 0.0 samples = 7 value = [7, 0] 3300->3301 3302 age <= 49.5 entropy = 0.341 samples = 189 value = [177, 12] 3300->3302 3303 age <= 45.5 entropy = 0.328 samples = 183 value = [172, 11] 3302->3303 3352 education <= 9.5 entropy = 0.65 samples = 6 value = [5, 1] 3302->3352 3304 age <= 41.5 entropy = 0.36 samples = 161 value = [150, 11] 3303->3304 3351 entropy = 0.0 samples = 22 value = [22, 0] 3303->3351 3305 education <= 8.5 entropy = 0.319 samples = 138 value = [130, 8] 3304->3305 3340 education <= 7.5 entropy = 0.559 samples = 23 value = [20, 3] 3304->3340 3306 entropy = 0.0 samples = 16 value = [16, 0] 3305->3306 3307 race_White <= 0.5 entropy = 0.349 samples = 122 value = [114, 8] 3305->3307 3308 entropy = 0.0 samples = 5 value = [5, 0] 3307->3308 3309 education <= 9.5 entropy = 0.36 samples = 117 value = [109, 8] 3307->3309 3310 age <= 33.5 entropy = 0.317 samples = 87 value = [82, 5] 3309->3310 3329 age <= 32.5 entropy = 0.469 samples = 30 value = [27, 3] 3309->3329 3311 entropy = 0.0 samples = 19 value = [19, 0] 3310->3311 3312 age <= 39.5 entropy = 0.379 samples = 68 value = [63, 5] 3310->3312 3313 workclass_Private <= 0.5 entropy = 0.475 samples = 49 value = [44, 5] 3312->3313 3328 entropy = 0.0 samples = 19 value = [19, 0] 3312->3328 3314 age <= 36.0 entropy = 0.811 samples = 4 value = [3, 1] 3313->3314 3317 age <= 34.5 entropy = 0.433 samples = 45 value = [41, 4] 3313->3317 3315 entropy = 0.0 samples = 1 value = [0, 1] 3314->3315 3316 entropy = 0.0 samples = 3 value = [3, 0] 3314->3316 3318 entropy = 0.65 samples = 6 value = [5, 1] 3317->3318 3319 age <= 36.5 entropy = 0.391 samples = 39 value = [36, 3] 3317->3319 3320 entropy = 0.0 samples = 10 value = [10, 0] 3319->3320 3321 age <= 38.5 entropy = 0.48 samples = 29 value = [26, 3] 3319->3321 3322 hours-per-week <= 41.0 entropy = 0.426 samples = 23 value = [21, 2] 3321->3322 3327 entropy = 0.65 samples = 6 value = [5, 1] 3321->3327 3323 age <= 37.5 entropy = 0.439 samples = 22 value = [20, 2] 3322->3323 3326 entropy = 0.0 samples = 1 value = [1, 0] 3322->3326 3324 entropy = 0.503 samples = 9 value = [8, 1] 3323->3324 3325 entropy = 0.391 samples = 13 value = [12, 1] 3323->3325 3330 entropy = 0.918 samples = 3 value = [2, 1] 3329->3330 3331 age <= 36.5 entropy = 0.381 samples = 27 value = [25, 2] 3329->3331 3332 entropy = 0.0 samples = 12 value = [12, 0] 3331->3332 3333 age <= 37.5 entropy = 0.567 samples = 15 value = [13, 2] 3331->3333 3334 entropy = 0.0 samples = 1 value = [0, 1] 3333->3334 3335 age <= 40.5 entropy = 0.371 samples = 14 value = [13, 1] 3333->3335 3336 entropy = 0.0 samples = 11 value = [11, 0] 3335->3336 3337 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3335->3337 3338 entropy = 0.0 samples = 2 value = [2, 0] 3337->3338 3339 entropy = 0.0 samples = 1 value = [0, 1] 3337->3339 3341 entropy = 0.0 samples = 1 value = [0, 1] 3340->3341 3342 race_Asian <= 0.5 entropy = 0.439 samples = 22 value = [20, 2] 3340->3342 3343 age <= 43.5 entropy = 0.276 samples = 21 value = [20, 1] 3342->3343 3350 entropy = 0.0 samples = 1 value = [0, 1] 3342->3350 3344 entropy = 0.0 samples = 9 value = [9, 0] 3343->3344 3345 age <= 44.5 entropy = 0.414 samples = 12 value = [11, 1] 3343->3345 3346 education <= 9.5 entropy = 0.592 samples = 7 value = [6, 1] 3345->3346 3349 entropy = 0.0 samples = 5 value = [5, 0] 3345->3349 3347 entropy = 0.918 samples = 3 value = [2, 1] 3346->3347 3348 entropy = 0.0 samples = 4 value = [4, 0] 3346->3348 3353 workclass_Private <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 3352->3353 3356 entropy = 0.0 samples = 4 value = [4, 0] 3352->3356 3354 entropy = 0.0 samples = 1 value = [1, 0] 3353->3354 3355 entropy = 0.0 samples = 1 value = [0, 1] 3353->3355 3358 education <= 9.5 entropy = 0.592 samples = 7 value = [6, 1] 3357->3358 3363 entropy = 0.0 samples = 38 value = [38, 0] 3357->3363 3359 entropy = 0.0 samples = 4 value = [4, 0] 3358->3359 3360 age <= 32.5 entropy = 0.918 samples = 3 value = [2, 1] 3358->3360 3361 entropy = 0.0 samples = 2 value = [2, 0] 3360->3361 3362 entropy = 0.0 samples = 1 value = [0, 1] 3360->3362 3365 education <= 11.5 entropy = 0.235 samples = 26 value = [25, 1] 3364->3365 3372 age <= 34.5 entropy = 0.954 samples = 8 value = [5, 3] 3364->3372 3366 entropy = 0.0 samples = 15 value = [15, 0] 3365->3366 3367 age <= 41.5 entropy = 0.439 samples = 11 value = [10, 1] 3365->3367 3368 age <= 39.5 entropy = 0.722 samples = 5 value = [4, 1] 3367->3368 3371 entropy = 0.0 samples = 6 value = [6, 0] 3367->3371 3369 entropy = 0.0 samples = 4 value = [4, 0] 3368->3369 3370 entropy = 0.0 samples = 1 value = [0, 1] 3368->3370 3373 entropy = 0.0 samples = 3 value = [3, 0] 3372->3373 3374 education <= 11.5 entropy = 0.971 samples = 5 value = [2, 3] 3372->3374 3375 entropy = 0.0 samples = 2 value = [0, 2] 3374->3375 3376 hours-per-week <= 39.0 entropy = 0.918 samples = 3 value = [2, 1] 3374->3376 3377 entropy = 0.0 samples = 1 value = [0, 1] 3376->3377 3378 entropy = 0.0 samples = 2 value = [2, 0] 3376->3378 3380 race_Hispanic <= 0.5 entropy = 0.538 samples = 65 value = [57, 8] 3379->3380 3423 entropy = 0.0 samples = 1 value = [0, 1] 3379->3423 3381 education <= 9.5 entropy = 0.498 samples = 64 value = [57, 7] 3380->3381 3422 entropy = 0.0 samples = 1 value = [0, 1] 3380->3422 3382 education <= 4.5 entropy = 0.348 samples = 46 value = [43, 3] 3381->3382 3403 workclass_Private <= 0.5 entropy = 0.764 samples = 18 value = [14, 4] 3381->3403 3383 entropy = 0.0 samples = 9 value = [9, 0] 3382->3383 3384 education <= 5.5 entropy = 0.406 samples = 37 value = [34, 3] 3382->3384 3385 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3384->3385 3390 age <= 53.5 entropy = 0.323 samples = 34 value = [32, 2] 3384->3390 3386 age <= 58.5 entropy = 1.0 samples = 2 value = [1, 1] 3385->3386 3389 entropy = 0.0 samples = 1 value = [1, 0] 3385->3389 3387 entropy = 0.0 samples = 1 value = [1, 0] 3386->3387 3388 entropy = 0.0 samples = 1 value = [0, 1] 3386->3388 3391 hours-per-week <= 39.0 entropy = 0.567 samples = 15 value = [13, 2] 3390->3391 3402 entropy = 0.0 samples = 19 value = [19, 0] 3390->3402 3392 entropy = 0.0 samples = 3 value = [3, 0] 3391->3392 3393 education <= 8.5 entropy = 0.65 samples = 12 value = [10, 2] 3391->3393 3394 entropy = 0.0 samples = 3 value = [3, 0] 3393->3394 3395 workclass_Self-emp <= 0.5 entropy = 0.764 samples = 9 value = [7, 2] 3393->3395 3396 age <= 52.5 entropy = 0.592 samples = 7 value = [6, 1] 3395->3396 3399 age <= 51.5 entropy = 1.0 samples = 2 value = [1, 1] 3395->3399 3397 entropy = 0.0 samples = 4 value = [4, 0] 3396->3397 3398 entropy = 0.918 samples = 3 value = [2, 1] 3396->3398 3400 entropy = 0.0 samples = 1 value = [0, 1] 3399->3400 3401 entropy = 0.0 samples = 1 value = [1, 0] 3399->3401 3404 entropy = 0.0 samples = 5 value = [5, 0] 3403->3404 3405 age <= 57.0 entropy = 0.89 samples = 13 value = [9, 4] 3403->3405 3406 age <= 55.0 entropy = 0.985 samples = 7 value = [4, 3] 3405->3406 3415 age <= 59.5 entropy = 0.65 samples = 6 value = [5, 1] 3405->3415 3407 age <= 51.5 entropy = 0.918 samples = 6 value = [4, 2] 3406->3407 3414 entropy = 0.0 samples = 1 value = [0, 1] 3406->3414 3408 entropy = 0.0 samples = 1 value = [1, 0] 3407->3408 3409 age <= 52.5 entropy = 0.971 samples = 5 value = [3, 2] 3407->3409 3410 entropy = 1.0 samples = 2 value = [1, 1] 3409->3410 3411 age <= 53.5 entropy = 0.918 samples = 3 value = [2, 1] 3409->3411 3412 entropy = 0.0 samples = 1 value = [1, 0] 3411->3412 3413 entropy = 1.0 samples = 2 value = [1, 1] 3411->3413 3416 entropy = 0.0 samples = 3 value = [3, 0] 3415->3416 3417 age <= 65.0 entropy = 0.918 samples = 3 value = [2, 1] 3415->3417 3418 hours-per-week <= 41.0 entropy = 1.0 samples = 2 value = [1, 1] 3417->3418 3421 entropy = 0.0 samples = 1 value = [1, 0] 3417->3421 3419 entropy = 0.0 samples = 1 value = [0, 1] 3418->3419 3420 entropy = 0.0 samples = 1 value = [1, 0] 3418->3420 3425 age <= 46.5 entropy = 0.052 samples = 172 value = [171, 1] 3424->3425 3434 age <= 33.5 entropy = 0.323 samples = 17 value = [16, 1] 3424->3434 3426 entropy = 0.0 samples = 115 value = [115, 0] 3425->3426 3427 age <= 47.5 entropy = 0.127 samples = 57 value = [56, 1] 3425->3427 3428 workclass_Private <= 0.5 entropy = 0.439 samples = 11 value = [10, 1] 3427->3428 3433 entropy = 0.0 samples = 46 value = [46, 0] 3427->3433 3429 entropy = 0.0 samples = 6 value = [6, 0] 3428->3429 3430 sex_Female <= 0.5 entropy = 0.722 samples = 5 value = [4, 1] 3428->3430 3431 entropy = 0.0 samples = 2 value = [2, 0] 3430->3431 3432 entropy = 0.918 samples = 3 value = [2, 1] 3430->3432 3435 education <= 11.5 entropy = 0.918 samples = 3 value = [2, 1] 3434->3435 3440 entropy = 0.0 samples = 14 value = [14, 0] 3434->3440 3436 workclass_Public <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 3435->3436 3439 entropy = 0.0 samples = 1 value = [1, 0] 3435->3439 3437 entropy = 0.0 samples = 1 value = [1, 0] 3436->3437 3438 entropy = 0.0 samples = 1 value = [0, 1] 3436->3438 3442 education <= 10.5 entropy = 0.355 samples = 149 value = [139, 10] 3441->3442 3489 race_White <= 0.5 entropy = 0.559 samples = 253 value = [220, 33] 3441->3489 3443 hours-per-week <= 51.0 entropy = 0.244 samples = 124 value = [119, 5] 3442->3443 3472 workclass_Public <= 0.5 entropy = 0.722 samples = 25 value = [20, 5] 3442->3472 3444 hours-per-week <= 49.5 entropy = 0.337 samples = 80 value = [75, 5] 3443->3444 3471 entropy = 0.0 samples = 44 value = [44, 0] 3443->3471 3445 hours-per-week <= 44.5 entropy = 0.144 samples = 49 value = [48, 1] 3444->3445 3452 age <= 44.5 entropy = 0.555 samples = 31 value = [27, 4] 3444->3452 3446 age <= 40.5 entropy = 0.414 samples = 12 value = [11, 1] 3445->3446 3451 entropy = 0.0 samples = 37 value = [37, 0] 3445->3451 3447 age <= 39.0 entropy = 0.918 samples = 3 value = [2, 1] 3446->3447 3450 entropy = 0.0 samples = 9 value = [9, 0] 3446->3450 3448 entropy = 0.0 samples = 1 value = [1, 0] 3447->3448 3449 entropy = 1.0 samples = 2 value = [1, 1] 3447->3449 3453 age <= 32.5 entropy = 0.297 samples = 19 value = [18, 1] 3452->3453 3458 age <= 45.5 entropy = 0.811 samples = 12 value = [9, 3] 3452->3458 3454 education <= 9.5 entropy = 0.918 samples = 3 value = [2, 1] 3453->3454 3457 entropy = 0.0 samples = 16 value = [16, 0] 3453->3457 3455 entropy = 0.0 samples = 1 value = [1, 0] 3454->3455 3456 entropy = 1.0 samples = 2 value = [1, 1] 3454->3456 3459 entropy = 0.0 samples = 1 value = [0, 1] 3458->3459 3460 age <= 50.5 entropy = 0.684 samples = 11 value = [9, 2] 3458->3460 3461 entropy = 0.0 samples = 3 value = [3, 0] 3460->3461 3462 age <= 55.5 entropy = 0.811 samples = 8 value = [6, 2] 3460->3462 3463 age <= 53.5 entropy = 0.971 samples = 5 value = [3, 2] 3462->3463 3470 entropy = 0.0 samples = 3 value = [3, 0] 3462->3470 3464 workclass_Private <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 3463->3464 3469 entropy = 0.0 samples = 1 value = [0, 1] 3463->3469 3465 entropy = 0.0 samples = 2 value = [2, 0] 3464->3465 3466 age <= 51.5 entropy = 1.0 samples = 2 value = [1, 1] 3464->3466 3467 entropy = 0.0 samples = 1 value = [0, 1] 3466->3467 3468 entropy = 0.0 samples = 1 value = [1, 0] 3466->3468 3473 age <= 39.0 entropy = 0.592 samples = 21 value = [18, 3] 3472->3473 3486 hours-per-week <= 53.0 entropy = 1.0 samples = 4 value = [2, 2] 3472->3486 3474 race_White <= 0.5 entropy = 0.779 samples = 13 value = [10, 3] 3473->3474 3485 entropy = 0.0 samples = 8 value = [8, 0] 3473->3485 3475 hours-per-week <= 47.5 entropy = 1.0 samples = 4 value = [2, 2] 3474->3475 3480 hours-per-week <= 47.5 entropy = 0.503 samples = 9 value = [8, 1] 3474->3480 3476 entropy = 0.0 samples = 1 value = [1, 0] 3475->3476 3477 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 3475->3477 3478 entropy = 0.0 samples = 1 value = [1, 0] 3477->3478 3479 entropy = 0.0 samples = 2 value = [0, 2] 3477->3479 3481 education <= 11.5 entropy = 0.918 samples = 3 value = [2, 1] 3480->3481 3484 entropy = 0.0 samples = 6 value = [6, 0] 3480->3484 3482 entropy = 0.0 samples = 1 value = [0, 1] 3481->3482 3483 entropy = 0.0 samples = 2 value = [2, 0] 3481->3483 3487 entropy = 0.0 samples = 2 value = [2, 0] 3486->3487 3488 entropy = 0.0 samples = 2 value = [0, 2] 3486->3488 3490 entropy = 0.0 samples = 16 value = [16, 0] 3489->3490 3491 education <= 11.5 entropy = 0.582 samples = 237 value = [204, 33] 3489->3491 3492 age <= 39.5 entropy = 0.597 samples = 228 value = [195, 33] 3491->3492 3631 entropy = 0.0 samples = 9 value = [9, 0] 3491->3631 3493 education <= 8.5 entropy = 0.509 samples = 124 value = [110, 14] 3492->3493 3566 workclass_Public <= 0.5 entropy = 0.686 samples = 104 value = [85, 19] 3492->3566 3494 age <= 36.5 entropy = 0.9 samples = 19 value = [13, 6] 3493->3494 3509 hours-per-week <= 61.0 entropy = 0.389 samples = 105 value = [97, 8] 3493->3509 3495 age <= 32.5 entropy = 0.65 samples = 12 value = [10, 2] 3494->3495 3502 hours-per-week <= 46.5 entropy = 0.985 samples = 7 value = [3, 4] 3494->3502 3496 entropy = 0.0 samples = 1 value = [0, 1] 3495->3496 3497 education <= 7.5 entropy = 0.439 samples = 11 value = [10, 1] 3495->3497 3498 entropy = 0.0 samples = 9 value = [9, 0] 3497->3498 3499 age <= 35.5 entropy = 1.0 samples = 2 value = [1, 1] 3497->3499 3500 entropy = 0.0 samples = 1 value = [0, 1] 3499->3500 3501 entropy = 0.0 samples = 1 value = [1, 0] 3499->3501 3503 entropy = 0.0 samples = 1 value = [1, 0] 3502->3503 3504 hours-per-week <= 62.5 entropy = 0.918 samples = 6 value = [2, 4] 3502->3504 3505 entropy = 0.0 samples = 3 value = [0, 3] 3504->3505 3506 hours-per-week <= 77.5 entropy = 0.918 samples = 3 value = [2, 1] 3504->3506 3507 entropy = 0.0 samples = 2 value = [2, 0] 3506->3507 3508 entropy = 0.0 samples = 1 value = [0, 1] 3506->3508 3510 age <= 36.5 entropy = 0.429 samples = 91 value = [83, 8] 3509->3510 3565 entropy = 0.0 samples = 14 value = [14, 0] 3509->3565 3511 workclass_Public <= 0.5 entropy = 0.52 samples = 60 value = [53, 7] 3510->3511 3558 hours-per-week <= 53.5 entropy = 0.206 samples = 31 value = [30, 1] 3510->3558 3512 hours-per-week <= 57.5 entropy = 0.485 samples = 57 value = [51, 6] 3511->3512 3553 hours-per-week <= 52.5 entropy = 0.918 samples = 3 value = [2, 1] 3511->3553 3513 hours-per-week <= 51.0 entropy = 0.426 samples = 46 value = [42, 4] 3512->3513 3542 education <= 9.5 entropy = 0.684 samples = 11 value = [9, 2] 3512->3542 3514 hours-per-week <= 44.5 entropy = 0.454 samples = 42 value = [38, 4] 3513->3514 3541 entropy = 0.0 samples = 4 value = [4, 0] 3513->3541 3515 entropy = 0.0 samples = 3 value = [3, 0] 3514->3515 3516 age <= 34.5 entropy = 0.477 samples = 39 value = [35, 4] 3514->3516 3517 age <= 33.5 entropy = 0.575 samples = 22 value = [19, 3] 3516->3517 3534 hours-per-week <= 46.5 entropy = 0.323 samples = 17 value = [16, 1] 3516->3534 3518 education <= 10.5 entropy = 0.371 samples = 14 value = [13, 1] 3517->3518 3525 hours-per-week <= 47.5 entropy = 0.811 samples = 8 value = [6, 2] 3517->3525 3519 entropy = 0.0 samples = 9 value = [9, 0] 3518->3519 3520 hours-per-week <= 48.0 entropy = 0.722 samples = 5 value = [4, 1] 3518->3520 3521 entropy = 0.0 samples = 3 value = [3, 0] 3520->3521 3522 workclass_Private <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 3520->3522 3523 entropy = 0.0 samples = 1 value = [1, 0] 3522->3523 3524 entropy = 0.0 samples = 1 value = [0, 1] 3522->3524 3526 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 3525->3526 3529 workclass_Self-emp <= 0.5 entropy = 0.65 samples = 6 value = [5, 1] 3525->3529 3527 entropy = 0.0 samples = 1 value = [1, 0] 3526->3527 3528 entropy = 0.0 samples = 1 value = [0, 1] 3526->3528 3530 education <= 9.5 entropy = 0.811 samples = 4 value = [3, 1] 3529->3530 3533 entropy = 0.0 samples = 2 value = [2, 0] 3529->3533 3531 entropy = 0.0 samples = 1 value = [1, 0] 3530->3531 3532 entropy = 0.918 samples = 3 value = [2, 1] 3530->3532 3535 age <= 35.5 entropy = 0.503 samples = 9 value = [8, 1] 3534->3535 3540 entropy = 0.0 samples = 8 value = [8, 0] 3534->3540 3536 entropy = 0.0 samples = 4 value = [4, 0] 3535->3536 3537 education <= 9.5 entropy = 0.722 samples = 5 value = [4, 1] 3535->3537 3538 entropy = 0.918 samples = 3 value = [2, 1] 3537->3538 3539 entropy = 0.0 samples = 2 value = [2, 0] 3537->3539 3543 age <= 32.5 entropy = 0.811 samples = 8 value = [6, 2] 3542->3543 3552 entropy = 0.0 samples = 3 value = [3, 0] 3542->3552 3544 entropy = 0.0 samples = 2 value = [2, 0] 3543->3544 3545 workclass_Private <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 3543->3545 3546 entropy = 0.0 samples = 1 value = [1, 0] 3545->3546 3547 age <= 34.0 entropy = 0.971 samples = 5 value = [3, 2] 3545->3547 3548 entropy = 0.0 samples = 1 value = [0, 1] 3547->3548 3549 age <= 35.5 entropy = 0.811 samples = 4 value = [3, 1] 3547->3549 3550 entropy = 0.0 samples = 2 value = [2, 0] 3549->3550 3551 entropy = 1.0 samples = 2 value = [1, 1] 3549->3551 3554 age <= 33.5 entropy = 1.0 samples = 2 value = [1, 1] 3553->3554 3557 entropy = 0.0 samples = 1 value = [1, 0] 3553->3557 3555 entropy = 0.0 samples = 1 value = [1, 0] 3554->3555 3556 entropy = 0.0 samples = 1 value = [0, 1] 3554->3556 3559 entropy = 0.0 samples = 21 value = [21, 0] 3558->3559 3560 education <= 9.5 entropy = 0.469 samples = 10 value = [9, 1] 3558->3560 3561 entropy = 0.0 samples = 8 value = [8, 0] 3560->3561 3562 hours-per-week <= 56.5 entropy = 1.0 samples = 2 value = [1, 1] 3560->3562 3563 entropy = 0.0 samples = 1 value = [0, 1] 3562->3563 3564 entropy = 0.0 samples = 1 value = [1, 0] 3562->3564 3567 hours-per-week <= 49.0 entropy = 0.654 samples = 101 value = [84, 17] 3566->3567 3628 education <= 6.5 entropy = 0.918 samples = 3 value = [1, 2] 3566->3628 3568 hours-per-week <= 45.5 entropy = 0.811 samples = 36 value = [27, 9] 3567->3568 3599 hours-per-week <= 54.5 entropy = 0.538 samples = 65 value = [57, 8] 3567->3599 3569 age <= 60.5 entropy = 0.619 samples = 26 value = [22, 4] 3568->3569 3586 education <= 9.5 entropy = 1.0 samples = 10 value = [5, 5] 3568->3586 3570 hours-per-week <= 44.5 entropy = 0.529 samples = 25 value = [22, 3] 3569->3570 3585 entropy = 0.0 samples = 1 value = [0, 1] 3569->3585 3571 age <= 43.5 entropy = 1.0 samples = 2 value = [1, 1] 3570->3571 3574 education <= 9.5 entropy = 0.426 samples = 23 value = [21, 2] 3570->3574 3572 entropy = 0.0 samples = 1 value = [0, 1] 3571->3572 3573 entropy = 0.0 samples = 1 value = [1, 0] 3571->3573 3575 entropy = 0.0 samples = 10 value = [10, 0] 3574->3575 3576 age <= 43.5 entropy = 0.619 samples = 13 value = [11, 2] 3574->3576 3577 entropy = 0.0 samples = 5 value = [5, 0] 3576->3577 3578 age <= 44.5 entropy = 0.811 samples = 8 value = [6, 2] 3576->3578 3579 entropy = 0.0 samples = 1 value = [0, 1] 3578->3579 3580 age <= 49.5 entropy = 0.592 samples = 7 value = [6, 1] 3578->3580 3581 age <= 47.5 entropy = 0.811 samples = 4 value = [3, 1] 3580->3581 3584 entropy = 0.0 samples = 3 value = [3, 0] 3580->3584 3582 entropy = 0.0 samples = 2 value = [2, 0] 3581->3582 3583 entropy = 1.0 samples = 2 value = [1, 1] 3581->3583 3587 age <= 51.5 entropy = 0.722 samples = 5 value = [4, 1] 3586->3587 3594 age <= 42.0 entropy = 0.722 samples = 5 value = [1, 4] 3586->3594 3588 age <= 43.5 entropy = 0.918 samples = 3 value = [2, 1] 3587->3588 3593 entropy = 0.0 samples = 2 value = [2, 0] 3587->3593 3589 entropy = 0.0 samples = 1 value = [1, 0] 3588->3589 3590 education <= 6.5 entropy = 1.0 samples = 2 value = [1, 1] 3588->3590 3591 entropy = 0.0 samples = 1 value = [1, 0] 3590->3591 3592 entropy = 0.0 samples = 1 value = [0, 1] 3590->3592 3595 entropy = 0.0 samples = 2 value = [0, 2] 3594->3595 3596 age <= 48.0 entropy = 0.918 samples = 3 value = [1, 2] 3594->3596 3597 entropy = 1.0 samples = 2 value = [1, 1] 3596->3597 3598 entropy = 0.0 samples = 1 value = [0, 1] 3596->3598 3600 entropy = 0.0 samples = 28 value = [28, 0] 3599->3600 3601 age <= 58.0 entropy = 0.753 samples = 37 value = [29, 8] 3599->3601 3602 hours-per-week <= 72.5 entropy = 0.837 samples = 30 value = [22, 8] 3601->3602 3627 entropy = 0.0 samples = 7 value = [7, 0] 3601->3627 3603 age <= 56.5 entropy = 0.877 samples = 27 value = [19, 8] 3602->3603 3626 entropy = 0.0 samples = 3 value = [3, 0] 3602->3626 3604 education <= 8.0 entropy = 0.84 samples = 26 value = [19, 7] 3603->3604 3625 entropy = 0.0 samples = 1 value = [0, 1] 3603->3625 3605 entropy = 0.0 samples = 3 value = [3, 0] 3604->3605 3606 age <= 54.0 entropy = 0.887 samples = 23 value = [16, 7] 3604->3606 3607 hours-per-week <= 65.0 entropy = 0.845 samples = 22 value = [16, 6] 3606->3607 3624 entropy = 0.0 samples = 1 value = [0, 1] 3606->3624 3608 workclass_Self-emp <= 0.5 entropy = 0.764 samples = 18 value = [14, 4] 3607->3608 3621 education <= 9.5 entropy = 1.0 samples = 4 value = [2, 2] 3607->3621 3609 hours-per-week <= 56.5 entropy = 0.918 samples = 9 value = [6, 3] 3608->3609 3616 age <= 47.5 entropy = 0.503 samples = 9 value = [8, 1] 3608->3616 3610 age <= 49.5 entropy = 0.65 samples = 6 value = [5, 1] 3609->3610 3613 age <= 40.5 entropy = 0.918 samples = 3 value = [1, 2] 3609->3613 3611 entropy = 0.0 samples = 4 value = [4, 0] 3610->3611 3612 entropy = 1.0 samples = 2 value = [1, 1] 3610->3612 3614 entropy = 0.0 samples = 1 value = [1, 0] 3613->3614 3615 entropy = 0.0 samples = 2 value = [0, 2] 3613->3615 3617 entropy = 0.0 samples = 6 value = [6, 0] 3616->3617 3618 education <= 9.5 entropy = 0.918 samples = 3 value = [2, 1] 3616->3618 3619 entropy = 0.0 samples = 1 value = [0, 1] 3618->3619 3620 entropy = 0.0 samples = 2 value = [2, 0] 3618->3620 3622 entropy = 0.0 samples = 2 value = [2, 0] 3621->3622 3623 entropy = 0.0 samples = 2 value = [0, 2] 3621->3623 3629 entropy = 0.0 samples = 1 value = [1, 0] 3628->3629 3630 entropy = 0.0 samples = 2 value = [0, 2] 3628->3630 3633 hours-per-week <= 39.5 entropy = 0.228 samples = 352 value = [339, 13] 3632->3633 3710 education <= 14.5 entropy = 0.879 samples = 648 value = [455, 193] 3632->3710 3634 entropy = 0.0 samples = 87 value = [87, 0] 3633->3634 3635 workclass_Public <= 0.5 entropy = 0.282 samples = 265 value = [252, 13] 3633->3635 3636 education <= 13.5 entropy = 0.323 samples = 221 value = [208, 13] 3635->3636 3709 entropy = 0.0 samples = 44 value = [44, 0] 3635->3709 3637 age <= 22.5 entropy = 0.268 samples = 197 value = [188, 9] 3636->3637 3686 education <= 15.5 entropy = 0.65 samples = 24 value = [20, 4] 3636->3686 3638 hours-per-week <= 52.5 entropy = 0.619 samples = 13 value = [11, 2] 3637->3638 3647 sex_Male <= 0.5 entropy = 0.233 samples = 184 value = [177, 7] 3637->3647 3639 sex_Female <= 0.5 entropy = 0.469 samples = 10 value = [9, 1] 3638->3639 3644 hours-per-week <= 57.5 entropy = 0.918 samples = 3 value = [2, 1] 3638->3644 3640 entropy = 0.0 samples = 5 value = [5, 0] 3639->3640 3641 hours-per-week <= 45.0 entropy = 0.722 samples = 5 value = [4, 1] 3639->3641 3642 entropy = 0.811 samples = 4 value = [3, 1] 3641->3642 3643 entropy = 0.0 samples = 1 value = [1, 0] 3641->3643 3645 entropy = 1.0 samples = 2 value = [1, 1] 3644->3645 3646 entropy = 0.0 samples = 1 value = [1, 0] 3644->3646 3648 age <= 25.5 entropy = 0.097 samples = 80 value = [79, 1] 3647->3648 3657 hours-per-week <= 49.0 entropy = 0.318 samples = 104 value = [98, 6] 3647->3657 3649 age <= 24.5 entropy = 0.176 samples = 38 value = [37, 1] 3648->3649 3656 entropy = 0.0 samples = 42 value = [42, 0] 3648->3656 3650 entropy = 0.0 samples = 26 value = [26, 0] 3649->3650 3651 race_Asian <= 0.5 entropy = 0.414 samples = 12 value = [11, 1] 3649->3651 3652 hours-per-week <= 45.0 entropy = 0.469 samples = 10 value = [9, 1] 3651->3652 3655 entropy = 0.0 samples = 2 value = [2, 0] 3651->3655 3653 entropy = 0.544 samples = 8 value = [7, 1] 3652->3653 3654 entropy = 0.0 samples = 2 value = [2, 0] 3652->3654 3658 race_Asian <= 0.5 entropy = 0.238 samples = 77 value = [74, 3] 3657->3658 3673 age <= 24.5 entropy = 0.503 samples = 27 value = [24, 3] 3657->3673 3659 workclass_Private <= 0.5 entropy = 0.181 samples = 73 value = [71, 2] 3658->3659 3670 age <= 26.5 entropy = 0.811 samples = 4 value = [3, 1] 3658->3670 3660 age <= 23.5 entropy = 0.65 samples = 6 value = [5, 1] 3659->3660 3663 age <= 27.5 entropy = 0.112 samples = 67 value = [66, 1] 3659->3663 3661 entropy = 0.0 samples = 1 value = [0, 1] 3660->3661 3662 entropy = 0.0 samples = 5 value = [5, 0] 3660->3662 3664 entropy = 0.0 samples = 49 value = [49, 0] 3663->3664 3665 age <= 28.5 entropy = 0.31 samples = 18 value = [17, 1] 3663->3665 3666 hours-per-week <= 41.5 entropy = 0.503 samples = 9 value = [8, 1] 3665->3666 3669 entropy = 0.0 samples = 9 value = [9, 0] 3665->3669 3667 entropy = 0.722 samples = 5 value = [4, 1] 3666->3667 3668 entropy = 0.0 samples = 4 value = [4, 0] 3666->3668 3671 entropy = 0.0 samples = 3 value = [3, 0] 3670->3671 3672 entropy = 0.0 samples = 1 value = [0, 1] 3670->3672 3674 entropy = 0.0 samples = 11 value = [11, 0] 3673->3674 3675 hours-per-week <= 52.5 entropy = 0.696 samples = 16 value = [13, 3] 3673->3675 3676 age <= 26.5 entropy = 0.881 samples = 10 value = [7, 3] 3675->3676 3685 entropy = 0.0 samples = 6 value = [6, 0] 3675->3685 3677 age <= 25.5 entropy = 0.918 samples = 3 value = [1, 2] 3676->3677 3680 age <= 28.5 entropy = 0.592 samples = 7 value = [6, 1] 3676->3680 3678 entropy = 1.0 samples = 2 value = [1, 1] 3677->3678 3679 entropy = 0.0 samples = 1 value = [0, 1] 3677->3679 3681 entropy = 0.0 samples = 4 value = [4, 0] 3680->3681 3682 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3680->3682 3683 entropy = 1.0 samples = 2 value = [1, 1] 3682->3683 3684 entropy = 0.0 samples = 1 value = [1, 0] 3682->3684 3687 age <= 25.5 entropy = 0.742 samples = 19 value = [15, 4] 3686->3687 3708 entropy = 0.0 samples = 5 value = [5, 0] 3686->3708 3688 entropy = 0.0 samples = 4 value = [4, 0] 3687->3688 3689 education <= 14.5 entropy = 0.837 samples = 15 value = [11, 4] 3687->3689 3690 hours-per-week <= 45.0 entropy = 0.722 samples = 10 value = [8, 2] 3689->3690 3703 age <= 27.5 entropy = 0.971 samples = 5 value = [3, 2] 3689->3703 3691 workclass_Private <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 3690->3691 3702 entropy = 0.0 samples = 3 value = [3, 0] 3690->3702 3692 entropy = 0.0 samples = 1 value = [1, 0] 3691->3692 3693 race_Black <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 3691->3693 3694 sex_Male <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] 3693->3694 3701 entropy = 0.0 samples = 1 value = [1, 0] 3693->3701 3695 age <= 27.0 entropy = 0.918 samples = 3 value = [2, 1] 3694->3695 3698 age <= 26.5 entropy = 1.0 samples = 2 value = [1, 1] 3694->3698 3696 entropy = 0.0 samples = 1 value = [1, 0] 3695->3696 3697 entropy = 1.0 samples = 2 value = [1, 1] 3695->3697 3699 entropy = 0.0 samples = 1 value = [0, 1] 3698->3699 3700 entropy = 0.0 samples = 1 value = [1, 0] 3698->3700 3704 entropy = 0.0 samples = 1 value = [0, 1] 3703->3704 3705 workclass_Private <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 3703->3705 3706 entropy = 0.0 samples = 1 value = [0, 1] 3705->3706 3707 entropy = 0.0 samples = 3 value = [3, 0] 3705->3707 3711 hours-per-week <= 42.5 entropy = 0.821 samples = 582 value = [433, 149] 3710->3711 4168 hours-per-week <= 75.0 entropy = 0.918 samples = 66 value = [22, 44] 3710->4168 3712 age <= 44.5 entropy = 0.666 samples = 351 value = [290, 61] 3711->3712 3941 age <= 49.5 entropy = 0.959 samples = 231 value = [143, 88] 3711->3941 3713 hours-per-week <= 10.0 entropy = 0.557 samples = 239 value = [208, 31] 3712->3713 3848 hours-per-week <= 39.0 entropy = 0.838 samples = 112 value = [82, 30] 3712->3848 3714 entropy = 0.0 samples = 1 value = [0, 1] 3713->3714 3715 hours-per-week <= 31.0 entropy = 0.547 samples = 238 value = [208, 30] 3713->3715 3716 entropy = 0.0 samples = 21 value = [21, 0] 3715->3716 3717 age <= 37.5 entropy = 0.58 samples = 217 value = [187, 30] 3715->3717 3718 age <= 33.5 entropy = 0.653 samples = 113 value = [94, 19] 3717->3718 3797 age <= 38.5 entropy = 0.487 samples = 104 value = [93, 11] 3717->3797 3719 sex_Male <= 0.5 entropy = 0.451 samples = 53 value = [48, 5] 3718->3719 3742 workclass_Public <= 0.5 entropy = 0.784 samples = 60 value = [46, 14] 3718->3742 3720 age <= 30.5 entropy = 0.216 samples = 29 value = [28, 1] 3719->3720 3725 workclass_Private <= 0.5 entropy = 0.65 samples = 24 value = [20, 4] 3719->3725 3721 workclass_Private <= 0.5 entropy = 0.439 samples = 11 value = [10, 1] 3720->3721 3724 entropy = 0.0 samples = 18 value = [18, 0] 3720->3724 3722 entropy = 0.0 samples = 5 value = [5, 0] 3721->3722 3723 entropy = 0.65 samples = 6 value = [5, 1] 3721->3723 3726 race_Black <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] 3725->3726 3731 race_White <= 0.5 entropy = 0.485 samples = 19 value = [17, 2] 3725->3731 3727 age <= 30.5 entropy = 0.811 samples = 4 value = [3, 1] 3726->3727 3730 entropy = 0.0 samples = 1 value = [0, 1] 3726->3730 3728 entropy = 0.0 samples = 1 value = [0, 1] 3727->3728 3729 entropy = 0.0 samples = 3 value = [3, 0] 3727->3729 3732 entropy = 0.0 samples = 5 value = [5, 0] 3731->3732 3733 hours-per-week <= 39.0 entropy = 0.592 samples = 14 value = [12, 2] 3731->3733 3734 entropy = 0.0 samples = 2 value = [2, 0] 3733->3734 3735 age <= 31.5 entropy = 0.65 samples = 12 value = [10, 2] 3733->3735 3736 age <= 30.5 entropy = 0.918 samples = 3 value = [2, 1] 3735->3736 3739 age <= 32.5 entropy = 0.503 samples = 9 value = [8, 1] 3735->3739 3737 entropy = 0.0 samples = 1 value = [1, 0] 3736->3737 3738 entropy = 1.0 samples = 2 value = [1, 1] 3736->3738 3740 entropy = 0.0 samples = 5 value = [5, 0] 3739->3740 3741 entropy = 0.811 samples = 4 value = [3, 1] 3739->3741 3743 education <= 13.5 entropy = 0.863 samples = 42 value = [30, 12] 3742->3743 3784 hours-per-week <= 39.0 entropy = 0.503 samples = 18 value = [16, 2] 3742->3784 3744 hours-per-week <= 41.0 entropy = 0.822 samples = 35 value = [26, 9] 3743->3744 3775 race_White <= 0.5 entropy = 0.985 samples = 7 value = [4, 3] 3743->3775 3745 hours-per-week <= 33.5 entropy = 0.834 samples = 34 value = [25, 9] 3744->3745 3774 entropy = 0.0 samples = 1 value = [1, 0] 3744->3774 3746 age <= 36.0 entropy = 1.0 samples = 2 value = [1, 1] 3745->3746 3749 hours-per-week <= 39.0 entropy = 0.811 samples = 32 value = [24, 8] 3745->3749 3747 entropy = 0.0 samples = 1 value = [1, 0] 3746->3747 3748 entropy = 0.0 samples = 1 value = [0, 1] 3746->3748 3750 entropy = 0.0 samples = 4 value = [4, 0] 3749->3750 3751 sex_Male <= 0.5 entropy = 0.863 samples = 28 value = [20, 8] 3749->3751 3752 workclass_Private <= 0.5 entropy = 0.742 samples = 19 value = [15, 4] 3751->3752 3765 workclass_Self-emp <= 0.5 entropy = 0.991 samples = 9 value = [5, 4] 3751->3765 3753 entropy = 0.0 samples = 1 value = [0, 1] 3752->3753 3754 age <= 35.5 entropy = 0.65 samples = 18 value = [15, 3] 3752->3754 3755 race_Black <= 0.5 entropy = 0.845 samples = 11 value = [8, 3] 3754->3755 3764 entropy = 0.0 samples = 7 value = [7, 0] 3754->3764 3756 race_Asian <= 0.5 entropy = 0.881 samples = 10 value = [7, 3] 3755->3756 3763 entropy = 0.0 samples = 1 value = [1, 0] 3755->3763 3757 age <= 34.5 entropy = 0.811 samples = 8 value = [6, 2] 3756->3757 3760 age <= 34.5 entropy = 1.0 samples = 2 value = [1, 1] 3756->3760 3758 entropy = 0.918 samples = 3 value = [2, 1] 3757->3758 3759 entropy = 0.722 samples = 5 value = [4, 1] 3757->3759 3761 entropy = 0.0 samples = 1 value = [1, 0] 3760->3761 3762 entropy = 0.0 samples = 1 value = [0, 1] 3760->3762 3766 age <= 36.5 entropy = 0.918 samples = 6 value = [2, 4] 3765->3766 3773 entropy = 0.0 samples = 3 value = [3, 0] 3765->3773 3767 age <= 34.5 entropy = 0.971 samples = 5 value = [2, 3] 3766->3767 3772 entropy = 0.0 samples = 1 value = [0, 1] 3766->3772 3768 entropy = 1.0 samples = 2 value = [1, 1] 3767->3768 3769 age <= 35.5 entropy = 0.918 samples = 3 value = [1, 2] 3767->3769 3770 entropy = 0.0 samples = 1 value = [0, 1] 3769->3770 3771 entropy = 1.0 samples = 2 value = [1, 1] 3769->3771 3776 entropy = 0.0 samples = 2 value = [2, 0] 3775->3776 3777 age <= 35.5 entropy = 0.971 samples = 5 value = [2, 3] 3775->3777 3778 entropy = 0.0 samples = 2 value = [0, 2] 3777->3778 3779 age <= 36.5 entropy = 0.918 samples = 3 value = [2, 1] 3777->3779 3780 entropy = 0.0 samples = 1 value = [1, 0] 3779->3780 3781 workclass_Private <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 3779->3781 3782 entropy = 0.0 samples = 1 value = [1, 0] 3781->3782 3783 entropy = 0.0 samples = 1 value = [0, 1] 3781->3783 3785 entropy = 0.0 samples = 5 value = [5, 0] 3784->3785 3786 age <= 36.5 entropy = 0.619 samples = 13 value = [11, 2] 3784->3786 3787 sex_Male <= 0.5 entropy = 0.439 samples = 11 value = [10, 1] 3786->3787 3794 sex_Male <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 3786->3794 3788 entropy = 0.0 samples = 7 value = [7, 0] 3787->3788 3789 education <= 13.5 entropy = 0.811 samples = 4 value = [3, 1] 3787->3789 3790 entropy = 0.0 samples = 2 value = [2, 0] 3789->3790 3791 age <= 35.5 entropy = 1.0 samples = 2 value = [1, 1] 3789->3791 3792 entropy = 0.0 samples = 1 value = [0, 1] 3791->3792 3793 entropy = 0.0 samples = 1 value = [1, 0] 3791->3793 3795 entropy = 0.0 samples = 1 value = [0, 1] 3794->3795 3796 entropy = 0.0 samples = 1 value = [1, 0] 3794->3796 3798 entropy = 0.0 samples = 19 value = [19, 0] 3797->3798 3799 workclass_Self-emp <= 0.5 entropy = 0.556 samples = 85 value = [74, 11] 3797->3799 3800 hours-per-week <= 38.5 entropy = 0.592 samples = 77 value = [66, 11] 3799->3800 3847 entropy = 0.0 samples = 8 value = [8, 0] 3799->3847 3801 sex_Female <= 0.5 entropy = 0.811 samples = 12 value = [9, 3] 3800->3801 3814 age <= 42.5 entropy = 0.538 samples = 65 value = [57, 8] 3800->3814 3802 entropy = 0.0 samples = 4 value = [4, 0] 3801->3802 3803 age <= 40.5 entropy = 0.954 samples = 8 value = [5, 3] 3801->3803 3804 entropy = 0.0 samples = 2 value = [2, 0] 3803->3804 3805 hours-per-week <= 33.5 entropy = 1.0 samples = 6 value = [3, 3] 3803->3805 3806 entropy = 0.0 samples = 1 value = [1, 0] 3805->3806 3807 workclass_Private <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 3805->3807 3808 age <= 41.5 entropy = 0.918 samples = 3 value = [2, 1] 3807->3808 3813 entropy = 0.0 samples = 2 value = [0, 2] 3807->3813 3809 hours-per-week <= 35.5 entropy = 1.0 samples = 2 value = [1, 1] 3808->3809 3812 entropy = 0.0 samples = 1 value = [1, 0] 3808->3812 3810 entropy = 0.0 samples = 1 value = [1, 0] 3809->3810 3811 entropy = 0.0 samples = 1 value = [0, 1] 3809->3811 3815 age <= 40.5 entropy = 0.391 samples = 39 value = [36, 3] 3814->3815 3830 sex_Female <= 0.5 entropy = 0.706 samples = 26 value = [21, 5] 3814->3830 3816 education <= 13.5 entropy = 0.696 samples = 16 value = [13, 3] 3815->3816 3829 entropy = 0.0 samples = 23 value = [23, 0] 3815->3829 3817 race_Black <= 0.5 entropy = 0.811 samples = 12 value = [9, 3] 3816->3817 3828 entropy = 0.0 samples = 4 value = [4, 0] 3816->3828 3818 sex_Female <= 0.5 entropy = 0.845 samples = 11 value = [8, 3] 3817->3818 3827 entropy = 0.0 samples = 1 value = [1, 0] 3817->3827 3819 workclass_Public <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 3818->3819 3822 age <= 39.5 entropy = 0.722 samples = 5 value = [4, 1] 3818->3822 3820 entropy = 0.811 samples = 4 value = [3, 1] 3819->3820 3821 entropy = 1.0 samples = 2 value = [1, 1] 3819->3821 3823 entropy = 0.0 samples = 2 value = [2, 0] 3822->3823 3824 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3822->3824 3825 entropy = 1.0 samples = 2 value = [1, 1] 3824->3825 3826 entropy = 0.0 samples = 1 value = [1, 0] 3824->3826 3831 race_White <= 0.5 entropy = 0.863 samples = 14 value = [10, 4] 3830->3831 3842 race_Black <= 0.5 entropy = 0.414 samples = 12 value = [11, 1] 3830->3842 3832 entropy = 0.0 samples = 2 value = [2, 0] 3831->3832 3833 education <= 13.5 entropy = 0.918 samples = 12 value = [8, 4] 3831->3833 3834 age <= 43.5 entropy = 0.971 samples = 10 value = [6, 4] 3833->3834 3841 entropy = 0.0 samples = 2 value = [2, 0] 3833->3841 3835 workclass_Public <= 0.5 entropy = 1.0 samples = 6 value = [3, 3] 3834->3835 3838 workclass_Private <= 0.5 entropy = 0.811 samples = 4 value = [3, 1] 3834->3838 3836 entropy = 0.811 samples = 4 value = [3, 1] 3835->3836 3837 entropy = 0.0 samples = 2 value = [0, 2] 3835->3837 3839 entropy = 0.0 samples = 2 value = [2, 0] 3838->3839 3840 entropy = 1.0 samples = 2 value = [1, 1] 3838->3840 3843 entropy = 0.0 samples = 10 value = [10, 0] 3842->3843 3844 age <= 43.5 entropy = 1.0 samples = 2 value = [1, 1] 3842->3844 3845 entropy = 0.0 samples = 1 value = [0, 1] 3844->3845 3846 entropy = 0.0 samples = 1 value = [1, 0] 3844->3846 3849 age <= 74.5 entropy = 0.431 samples = 34 value = [31, 3] 3848->3849 3862 workclass_Private <= 0.5 entropy = 0.931 samples = 78 value = [51, 27] 3848->3862 3850 hours-per-week <= 33.5 entropy = 0.33 samples = 33 value = [31, 2] 3849->3850 3861 entropy = 0.0 samples = 1 value = [0, 1] 3849->3861 3851 entropy = 0.0 samples = 20 value = [20, 0] 3850->3851 3852 hours-per-week <= 35.5 entropy = 0.619 samples = 13 value = [11, 2] 3850->3852 3853 workclass_Private <= 0.5 entropy = 0.811 samples = 8 value = [6, 2] 3852->3853 3860 entropy = 0.0 samples = 5 value = [5, 0] 3852->3860 3854 entropy = 0.0 samples = 1 value = [0, 1] 3853->3854 3855 age <= 46.5 entropy = 0.592 samples = 7 value = [6, 1] 3853->3855 3856 sex_Male <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 3855->3856 3859 entropy = 0.0 samples = 5 value = [5, 0] 3855->3859 3857 entropy = 0.0 samples = 1 value = [1, 0] 3856->3857 3858 entropy = 0.0 samples = 1 value = [0, 1] 3856->3858 3863 race_Asian <= 0.5 entropy = 0.985 samples = 35 value = [20, 15] 3862->3863 3902 age <= 60.5 entropy = 0.854 samples = 43 value = [31, 12] 3862->3902 3864 hours-per-week <= 41.0 entropy = 0.977 samples = 34 value = [20, 14] 3863->3864 3901 entropy = 0.0 samples = 1 value = [0, 1] 3863->3901 3865 age <= 45.5 entropy = 0.967 samples = 33 value = [20, 13] 3864->3865 3900 entropy = 0.0 samples = 1 value = [0, 1] 3864->3900 3866 entropy = 0.0 samples = 4 value = [4, 0] 3865->3866 3867 age <= 54.5 entropy = 0.992 samples = 29 value = [16, 13] 3865->3867 3868 age <= 52.0 entropy = 0.998 samples = 21 value = [10, 11] 3867->3868 3891 age <= 61.0 entropy = 0.811 samples = 8 value = [6, 2] 3867->3891 3869 age <= 50.5 entropy = 1.0 samples = 20 value = [10, 10] 3868->3869 3890 entropy = 0.0 samples = 1 value = [0, 1] 3868->3890 3870 age <= 49.5 entropy = 0.998 samples = 19 value = [9, 10] 3869->3870 3889 entropy = 0.0 samples = 1 value = [1, 0] 3869->3889 3871 age <= 48.5 entropy = 0.997 samples = 15 value = [8, 7] 3870->3871 3886 sex_Male <= 0.5 entropy = 0.811 samples = 4 value = [1, 3] 3870->3886 3872 age <= 47.5 entropy = 0.971 samples = 10 value = [4, 6] 3871->3872 3881 education <= 13.5 entropy = 0.722 samples = 5 value = [4, 1] 3871->3881 3873 education <= 13.5 entropy = 1.0 samples = 8 value = [4, 4] 3872->3873 3880 entropy = 0.0 samples = 2 value = [0, 2] 3872->3880 3874 race_White <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] 3873->3874 3879 entropy = 0.0 samples = 2 value = [2, 0] 3873->3879 3875 entropy = 0.0 samples = 1 value = [1, 0] 3874->3875 3876 age <= 46.5 entropy = 0.722 samples = 5 value = [1, 4] 3874->3876 3877 entropy = 0.918 samples = 3 value = [1, 2] 3876->3877 3878 entropy = 0.0 samples = 2 value = [0, 2] 3876->3878 3882 entropy = 0.0 samples = 2 value = [2, 0] 3881->3882 3883 race_White <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3881->3883 3884 entropy = 0.0 samples = 1 value = [0, 1] 3883->3884 3885 entropy = 0.0 samples = 2 value = [2, 0] 3883->3885 3887 entropy = 1.0 samples = 2 value = [1, 1] 3886->3887 3888 entropy = 0.0 samples = 2 value = [0, 2] 3886->3888 3892 entropy = 0.0 samples = 4 value = [4, 0] 3891->3892 3893 race_White <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 3891->3893 3894 entropy = 0.0 samples = 1 value = [1, 0] 3893->3894 3895 workclass_Public <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 3893->3895 3896 sex_Female <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 3895->3896 3899 entropy = 0.0 samples = 1 value = [0, 1] 3895->3899 3897 entropy = 0.0 samples = 1 value = [1, 0] 3896->3897 3898 entropy = 0.0 samples = 1 value = [0, 1] 3896->3898 3903 age <= 58.5 entropy = 0.8 samples = 37 value = [28, 9] 3902->3903 3934 age <= 62.0 entropy = 1.0 samples = 6 value = [3, 3] 3902->3934 3904 age <= 57.5 entropy = 0.834 samples = 34 value = [25, 9] 3903->3904 3933 entropy = 0.0 samples = 3 value = [3, 0] 3903->3933 3905 sex_Male <= 0.5 entropy = 0.799 samples = 33 value = [25, 8] 3904->3905 3932 entropy = 0.0 samples = 1 value = [0, 1] 3904->3932 3906 education <= 13.5 entropy = 0.523 samples = 17 value = [15, 2] 3905->3906 3913 age <= 46.5 entropy = 0.954 samples = 16 value = [10, 6] 3905->3913 3907 entropy = 0.0 samples = 10 value = [10, 0] 3906->3907 3908 age <= 50.0 entropy = 0.863 samples = 7 value = [5, 2] 3906->3908 3909 race_Asian <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 3908->3909 3912 entropy = 0.0 samples = 4 value = [4, 0] 3908->3912 3910 entropy = 0.0 samples = 2 value = [0, 2] 3909->3910 3911 entropy = 0.0 samples = 1 value = [1, 0] 3909->3911 3914 age <= 45.5 entropy = 0.722 samples = 5 value = [4, 1] 3913->3914 3917 age <= 52.5 entropy = 0.994 samples = 11 value = [6, 5] 3913->3917 3915 entropy = 0.918 samples = 3 value = [2, 1] 3914->3915 3916 entropy = 0.0 samples = 2 value = [2, 0] 3914->3916 3918 age <= 51.5 entropy = 1.0 samples = 10 value = [5, 5] 3917->3918 3931 entropy = 0.0 samples = 1 value = [1, 0] 3917->3931 3919 hours-per-week <= 41.0 entropy = 0.991 samples = 9 value = [5, 4] 3918->3919 3930 entropy = 0.0 samples = 1 value = [0, 1] 3918->3930 3920 age <= 47.5 entropy = 1.0 samples = 8 value = [4, 4] 3919->3920 3929 entropy = 0.0 samples = 1 value = [1, 0] 3919->3929 3921 entropy = 0.0 samples = 1 value = [0, 1] 3920->3921 3922 age <= 50.5 entropy = 0.985 samples = 7 value = [4, 3] 3920->3922 3923 race_White <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 3922->3923 3926 race_Black <= 0.5 entropy = 1.0 samples = 4 value = [2, 2] 3922->3926 3924 entropy = 0.0 samples = 1 value = [1, 0] 3923->3924 3925 entropy = 1.0 samples = 2 value = [1, 1] 3923->3925 3927 entropy = 0.918 samples = 3 value = [2, 1] 3926->3927 3928 entropy = 0.0 samples = 1 value = [0, 1] 3926->3928 3935 entropy = 0.0 samples = 1 value = [0, 1] 3934->3935 3936 education <= 13.5 entropy = 0.971 samples = 5 value = [3, 2] 3934->3936 3937 entropy = 0.0 samples = 2 value = [2, 0] 3936->3937 3938 age <= 65.0 entropy = 0.918 samples = 3 value = [1, 2] 3936->3938 3939 entropy = 0.0 samples = 2 value = [0, 2] 3938->3939 3940 entropy = 0.0 samples = 1 value = [1, 0] 3938->3940 3942 education <= 13.5 entropy = 0.925 samples = 188 value = [124, 64] 3941->3942 4135 age <= 56.5 entropy = 0.99 samples = 43 value = [19, 24] 3941->4135 3943 race_Hispanic <= 0.5 entropy = 0.881 samples = 130 value = [91, 39] 3942->3943 4064 workclass_Public <= 0.5 entropy = 0.986 samples = 58 value = [33, 25] 3942->4064 3944 age <= 43.5 entropy = 0.875 samples = 129 value = [91, 38] 3943->3944 4063 entropy = 0.0 samples = 1 value = [0, 1] 3943->4063 3945 hours-per-week <= 67.5 entropy = 0.823 samples = 97 value = [72, 25] 3944->3945 4032 hours-per-week <= 47.0 entropy = 0.974 samples = 32 value = [19, 13] 3944->4032 3946 hours-per-week <= 46.0 entropy = 0.836 samples = 94 value = [69, 25] 3945->3946 4031 entropy = 0.0 samples = 3 value = [3, 0] 3945->4031 3947 age <= 41.0 entropy = 0.918 samples = 30 value = [20, 10] 3946->3947 3980 age <= 41.5 entropy = 0.786 samples = 64 value = [49, 15] 3946->3980 3948 age <= 30.5 entropy = 0.855 samples = 25 value = [18, 7] 3947->3948 3973 workclass_Private <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 3947->3973 3949 sex_Female <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 3948->3949 3954 age <= 32.5 entropy = 0.773 samples = 22 value = [17, 5] 3948->3954 3950 workclass_Private <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 3949->3950 3953 entropy = 0.0 samples = 1 value = [0, 1] 3949->3953 3951 entropy = 0.0 samples = 1 value = [0, 1] 3950->3951 3952 entropy = 0.0 samples = 1 value = [1, 0] 3950->3952 3955 entropy = 0.0 samples = 6 value = [6, 0] 3954->3955 3956 workclass_Public <= 0.5 entropy = 0.896 samples = 16 value = [11, 5] 3954->3956 3957 race_Black <= 0.5 entropy = 0.961 samples = 13 value = [8, 5] 3956->3957 3972 entropy = 0.0 samples = 3 value = [3, 0] 3956->3972 3958 sex_Female <= 0.5 entropy = 0.918 samples = 12 value = [8, 4] 3957->3958 3971 entropy = 0.0 samples = 1 value = [0, 1] 3957->3971 3959 age <= 36.0 entropy = 0.722 samples = 5 value = [4, 1] 3958->3959 3964 age <= 36.5 entropy = 0.985 samples = 7 value = [4, 3] 3958->3964 3960 age <= 34.5 entropy = 1.0 samples = 2 value = [1, 1] 3959->3960 3963 entropy = 0.0 samples = 3 value = [3, 0] 3959->3963 3961 entropy = 0.0 samples = 1 value = [1, 0] 3960->3961 3962 entropy = 0.0 samples = 1 value = [0, 1] 3960->3962 3965 age <= 33.5 entropy = 0.811 samples = 4 value = [3, 1] 3964->3965 3968 age <= 38.5 entropy = 0.918 samples = 3 value = [1, 2] 3964->3968 3966 entropy = 1.0 samples = 2 value = [1, 1] 3965->3966 3967 entropy = 0.0 samples = 2 value = [2, 0] 3965->3967 3969 entropy = 0.0 samples = 1 value = [0, 1] 3968->3969 3970 entropy = 1.0 samples = 2 value = [1, 1] 3968->3970 3974 entropy = 0.0 samples = 1 value = [0, 1] 3973->3974 3975 hours-per-week <= 44.5 entropy = 1.0 samples = 4 value = [2, 2] 3973->3975 3976 entropy = 0.0 samples = 1 value = [0, 1] 3975->3976 3977 age <= 42.5 entropy = 0.918 samples = 3 value = [2, 1] 3975->3977 3978 entropy = 0.0 samples = 1 value = [1, 0] 3977->3978 3979 entropy = 1.0 samples = 2 value = [1, 1] 3977->3979 3981 age <= 35.5 entropy = 0.831 samples = 57 value = [42, 15] 3980->3981 4030 entropy = 0.0 samples = 7 value = [7, 0] 3980->4030 3982 hours-per-week <= 49.0 entropy = 0.907 samples = 31 value = [21, 10] 3981->3982 4013 age <= 36.5 entropy = 0.706 samples = 26 value = [21, 5] 3981->4013 3983 entropy = 0.0 samples = 4 value = [4, 0] 3982->3983 3984 race_Asian <= 0.5 entropy = 0.951 samples = 27 value = [17, 10] 3982->3984 3985 workclass_Self-emp <= 0.5 entropy = 0.931 samples = 26 value = [17, 9] 3984->3985 4012 entropy = 0.0 samples = 1 value = [0, 1] 3984->4012 3986 sex_Female <= 0.5 entropy = 0.954 samples = 24 value = [15, 9] 3985->3986 4011 entropy = 0.0 samples = 2 value = [2, 0] 3985->4011 3987 race_Black <= 0.5 entropy = 0.896 samples = 16 value = [11, 5] 3986->3987 4000 age <= 30.5 entropy = 1.0 samples = 8 value = [4, 4] 3986->4000 3988 age <= 34.5 entropy = 0.837 samples = 15 value = [11, 4] 3987->3988 3999 entropy = 0.0 samples = 1 value = [0, 1] 3987->3999 3989 age <= 31.5 entropy = 0.722 samples = 10 value = [8, 2] 3988->3989 3998 entropy = 0.971 samples = 5 value = [3, 2] 3988->3998 3990 workclass_Private <= 0.5 entropy = 0.863 samples = 7 value = [5, 2] 3989->3990 3997 entropy = 0.0 samples = 3 value = [3, 0] 3989->3997 3991 entropy = 0.0 samples = 1 value = [0, 1] 3990->3991 3992 hours-per-week <= 52.5 entropy = 0.65 samples = 6 value = [5, 1] 3990->3992 3993 entropy = 0.0 samples = 4 value = [4, 0] 3992->3993 3994 age <= 30.5 entropy = 1.0 samples = 2 value = [1, 1] 3992->3994 3995 entropy = 0.0 samples = 1 value = [0, 1] 3994->3995 3996 entropy = 0.0 samples = 1 value = [1, 0] 3994->3996 4001 entropy = 0.0 samples = 1 value = [1, 0] 4000->4001 4002 age <= 34.0 entropy = 0.985 samples = 7 value = [3, 4] 4000->4002 4003 race_Black <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] 4002->4003 4010 entropy = 0.0 samples = 1 value = [1, 0] 4002->4010 4004 workclass_Public <= 0.5 entropy = 0.722 samples = 5 value = [1, 4] 4003->4004 4009 entropy = 0.0 samples = 1 value = [1, 0] 4003->4009 4005 entropy = 0.0 samples = 2 value = [0, 2] 4004->4005 4006 age <= 31.5 entropy = 0.918 samples = 3 value = [1, 2] 4004->4006 4007 entropy = 0.0 samples = 1 value = [1, 0] 4006->4007 4008 entropy = 0.0 samples = 2 value = [0, 2] 4006->4008 4014 entropy = 0.0 samples = 6 value = [6, 0] 4013->4014 4015 sex_Female <= 0.5 entropy = 0.811 samples = 20 value = [15, 5] 4013->4015 4016 age <= 37.5 entropy = 0.946 samples = 11 value = [7, 4] 4015->4016 4025 age <= 40.5 entropy = 0.503 samples = 9 value = [8, 1] 4015->4025 4017 entropy = 0.0 samples = 2 value = [0, 2] 4016->4017 4018 hours-per-week <= 49.0 entropy = 0.764 samples = 9 value = [7, 2] 4016->4018 4019 entropy = 0.0 samples = 1 value = [0, 1] 4018->4019 4020 hours-per-week <= 55.0 entropy = 0.544 samples = 8 value = [7, 1] 4018->4020 4021 entropy = 0.0 samples = 5 value = [5, 0] 4020->4021 4022 workclass_Private <= 0.5 entropy = 0.918 samples = 3 value = [2, 1] 4020->4022 4023 entropy = 0.0 samples = 2 value = [2, 0] 4022->4023 4024 entropy = 0.0 samples = 1 value = [0, 1] 4022->4024 4026 entropy = 0.0 samples = 7 value = [7, 0] 4025->4026 4027 hours-per-week <= 52.5 entropy = 1.0 samples = 2 value = [1, 1] 4025->4027 4028 entropy = 0.0 samples = 1 value = [0, 1] 4027->4028 4029 entropy = 0.0 samples = 1 value = [1, 0] 4027->4029 4033 sex_Male <= 0.5 entropy = 0.684 samples = 11 value = [9, 2] 4032->4033 4042 race_White <= 0.5 entropy = 0.998 samples = 21 value = [10, 11] 4032->4042 4034 entropy = 0.0 samples = 5 value = [5, 0] 4033->4034 4035 race_White <= 0.5 entropy = 0.918 samples = 6 value = [4, 2] 4033->4035 4036 entropy = 0.0 samples = 1 value = [0, 1] 4035->4036 4037 age <= 46.0 entropy = 0.722 samples = 5 value = [4, 1] 4035->4037 4038 hours-per-week <= 45.5 entropy = 1.0 samples = 2 value = [1, 1] 4037->4038 4041 entropy = 0.0 samples = 3 value = [3, 0] 4037->4041 4039 entropy = 0.0 samples = 1 value = [0, 1] 4038->4039 4040 entropy = 0.0 samples = 1 value = [1, 0] 4038->4040 4043 entropy = 0.0 samples = 2 value = [2, 0] 4042->4043 4044 sex_Male <= 0.5 entropy = 0.982 samples = 19 value = [8, 11] 4042->4044 4045 workclass_Private <= 0.5 entropy = 0.98 samples = 12 value = [7, 5] 4044->4045 4058 age <= 47.5 entropy = 0.592 samples = 7 value = [1, 6] 4044->4058 4046 age <= 47.5 entropy = 0.863 samples = 7 value = [5, 2] 4045->4046 4053 age <= 47.5 entropy = 0.971 samples = 5 value = [2, 3] 4045->4053 4047 hours-per-week <= 55.0 entropy = 1.0 samples = 4 value = [2, 2] 4046->4047 4052 entropy = 0.0 samples = 3 value = [3, 0] 4046->4052 4048 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 4047->4048 4051 entropy = 0.0 samples = 1 value = [1, 0] 4047->4051 4049 entropy = 0.0 samples = 2 value = [0, 2] 4048->4049 4050 entropy = 0.0 samples = 1 value = [1, 0] 4048->4050 4054 age <= 44.5 entropy = 0.918 samples = 3 value = [2, 1] 4053->4054 4057 entropy = 0.0 samples = 2 value = [0, 2] 4053->4057 4055 entropy = 0.0 samples = 1 value = [0, 1] 4054->4055 4056 entropy = 0.0 samples = 2 value = [2, 0] 4054->4056 4059 entropy = 0.0 samples = 5 value = [0, 5] 4058->4059 4060 workclass_Self-emp <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 4058->4060 4061 entropy = 0.0 samples = 1 value = [0, 1] 4060->4061 4062 entropy = 0.0 samples = 1 value = [1, 0] 4060->4062 4065 age <= 36.5 entropy = 0.995 samples = 35 value = [16, 19] 4064->4065 4112 age <= 39.5 entropy = 0.828 samples = 23 value = [17, 6] 4064->4112 4066 age <= 34.5 entropy = 0.764 samples = 9 value = [2, 7] 4065->4066 4075 hours-per-week <= 77.5 entropy = 0.996 samples = 26 value = [14, 12] 4065->4075 4067 age <= 30.5 entropy = 1.0 samples = 4 value = [2, 2] 4066->4067 4074 entropy = 0.0 samples = 5 value = [0, 5] 4066->4074 4068 entropy = 0.0 samples = 1 value = [0, 1] 4067->4068 4069 age <= 32.5 entropy = 0.918 samples = 3 value = [2, 1] 4067->4069 4070 hours-per-week <= 48.0 entropy = 1.0 samples = 2 value = [1, 1] 4069->4070 4073 entropy = 0.0 samples = 1 value = [1, 0] 4069->4073 4071 entropy = 0.0 samples = 1 value = [1, 0] 4070->4071 4072 entropy = 0.0 samples = 1 value = [0, 1] 4070->4072 4076 hours-per-week <= 62.5 entropy = 0.99 samples = 25 value = [14, 11] 4075->4076 4111 entropy = 0.0 samples = 1 value = [0, 1] 4075->4111 4077 race_White <= 0.5 entropy = 0.999 samples = 23 value = [12, 11] 4076->4077 4110 entropy = 0.0 samples = 2 value = [2, 0] 4076->4110 4078 entropy = 0.0 samples = 1 value = [0, 1] 4077->4078 4079 age <= 38.5 entropy = 0.994 samples = 22 value = [12, 10] 4077->4079 4080 workclass_Self-emp <= 0.5 entropy = 0.918 samples = 6 value = [2, 4] 4079->4080 4091 age <= 41.0 entropy = 0.954 samples = 16 value = [10, 6] 4079->4091 4081 sex_Female <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 4080->4081 4090 entropy = 0.0 samples = 1 value = [0, 1] 4080->4090 4082 hours-per-week <= 52.5 entropy = 0.918 samples = 3 value = [1, 2] 4081->4082 4087 age <= 37.5 entropy = 1.0 samples = 2 value = [1, 1] 4081->4087 4083 entropy = 0.0 samples = 1 value = [0, 1] 4082->4083 4084 age <= 37.5 entropy = 1.0 samples = 2 value = [1, 1] 4082->4084 4085 entropy = 0.0 samples = 1 value = [0, 1] 4084->4085 4086 entropy = 0.0 samples = 1 value = [1, 0] 4084->4086 4088 entropy = 0.0 samples = 1 value = [1, 0] 4087->4088 4089 entropy = 0.0 samples = 1 value = [0, 1] 4087->4089 4092 entropy = 0.0 samples = 3 value = [3, 0] 4091->4092 4093 age <= 42.5 entropy = 0.996 samples = 13 value = [7, 6] 4091->4093 4094 entropy = 0.0 samples = 1 value = [0, 1] 4093->4094 4095 age <= 44.5 entropy = 0.98 samples = 12 value = [7, 5] 4093->4095 4096 entropy = 0.0 samples = 2 value = [2, 0] 4095->4096 4097 workclass_Private <= 0.5 entropy = 1.0 samples = 10 value = [5, 5] 4095->4097 4098 entropy = 0.0 samples = 1 value = [1, 0] 4097->4098 4099 hours-per-week <= 46.5 entropy = 0.991 samples = 9 value = [4, 5] 4097->4099 4100 entropy = 0.0 samples = 1 value = [1, 0] 4099->4100 4101 age <= 48.0 entropy = 0.954 samples = 8 value = [3, 5] 4099->4101 4102 sex_Female <= 0.5 entropy = 0.863 samples = 7 value = [2, 5] 4101->4102 4109 entropy = 0.0 samples = 1 value = [1, 0] 4101->4109 4103 entropy = 0.0 samples = 2 value = [0, 2] 4102->4103 4104 age <= 46.5 entropy = 0.971 samples = 5 value = [2, 3] 4102->4104 4105 age <= 45.5 entropy = 1.0 samples = 4 value = [2, 2] 4104->4105 4108 entropy = 0.0 samples = 1 value = [0, 1] 4104->4108 4106 entropy = 1.0 samples = 2 value = [1, 1] 4105->4106 4107 entropy = 1.0 samples = 2 value = [1, 1] 4105->4107 4113 entropy = 0.0 samples = 6 value = [6, 0] 4112->4113 4114 race_Black <= 0.5 entropy = 0.937 samples = 17 value = [11, 6] 4112->4114 4115 hours-per-week <= 53.5 entropy = 0.896 samples = 16 value = [11, 5] 4114->4115 4134 entropy = 0.0 samples = 1 value = [0, 1] 4114->4134 4116 age <= 46.0 entropy = 0.544 samples = 8 value = [7, 1] 4115->4116 4121 age <= 40.5 entropy = 1.0 samples = 8 value = [4, 4] 4115->4121 4117 entropy = 0.0 samples = 4 value = [4, 0] 4116->4117 4118 hours-per-week <= 47.5 entropy = 0.811 samples = 4 value = [3, 1] 4116->4118 4119 entropy = 1.0 samples = 2 value = [1, 1] 4118->4119 4120 entropy = 0.0 samples = 2 value = [2, 0] 4118->4120 4122 entropy = 0.0 samples = 1 value = [0, 1] 4121->4122 4123 age <= 41.5 entropy = 0.985 samples = 7 value = [4, 3] 4121->4123 4124 entropy = 0.0 samples = 2 value = [2, 0] 4123->4124 4125 hours-per-week <= 58.0 entropy = 0.971 samples = 5 value = [2, 3] 4123->4125 4126 entropy = 0.0 samples = 1 value = [0, 1] 4125->4126 4127 age <= 43.5 entropy = 1.0 samples = 4 value = [2, 2] 4125->4127 4128 sex_Male <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 4127->4128 4133 entropy = 0.0 samples = 1 value = [1, 0] 4127->4133 4129 entropy = 0.0 samples = 1 value = [0, 1] 4128->4129 4130 age <= 42.5 entropy = 1.0 samples = 2 value = [1, 1] 4128->4130 4131 entropy = 0.0 samples = 1 value = [1, 0] 4130->4131 4132 entropy = 0.0 samples = 1 value = [0, 1] 4130->4132 4136 race_Amer-Indian <= 0.5 entropy = 0.881 samples = 30 value = [9, 21] 4135->4136 4163 sex_Female <= 0.5 entropy = 0.779 samples = 13 value = [10, 3] 4135->4163 4137 hours-per-week <= 57.5 entropy = 0.811 samples = 28 value = [7, 21] 4136->4137 4162 entropy = 0.0 samples = 2 value = [2, 0] 4136->4162 4138 education <= 13.5 entropy = 0.503 samples = 18 value = [2, 16] 4137->4138 4155 workclass_Private <= 0.5 entropy = 1.0 samples = 10 value = [5, 5] 4137->4155 4139 age <= 50.5 entropy = 0.592 samples = 14 value = [2, 12] 4138->4139 4154 entropy = 0.0 samples = 4 value = [0, 4] 4138->4154 4140 entropy = 0.0 samples = 3 value = [0, 3] 4139->4140 4141 age <= 54.5 entropy = 0.684 samples = 11 value = [2, 9] 4139->4141 4142 hours-per-week <= 44.0 entropy = 0.811 samples = 8 value = [2, 6] 4141->4142 4153 entropy = 0.0 samples = 3 value = [0, 3] 4141->4153 4143 entropy = 0.0 samples = 2 value = [0, 2] 4142->4143 4144 hours-per-week <= 52.5 entropy = 0.918 samples = 6 value = [2, 4] 4142->4144 4145 race_White <= 0.5 entropy = 0.971 samples = 5 value = [2, 3] 4144->4145 4152 entropy = 0.0 samples = 1 value = [0, 1] 4144->4152 4146 entropy = 0.0 samples = 1 value = [0, 1] 4145->4146 4147 hours-per-week <= 47.5 entropy = 1.0 samples = 4 value = [2, 2] 4145->4147 4148 entropy = 0.0 samples = 1 value = [1, 0] 4147->4148 4149 sex_Female <= 0.5 entropy = 0.918 samples = 3 value = [1, 2] 4147->4149 4150 entropy = 0.0 samples = 2 value = [0, 2] 4149->4150 4151 entropy = 0.0 samples = 1 value = [1, 0] 4149->4151 4156 age <= 53.5 entropy = 0.65 samples = 6 value = [1, 5] 4155->4156 4161 entropy = 0.0 samples = 4 value = [4, 0] 4155->4161 4157 entropy = 0.0 samples = 4 value = [0, 4] 4156->4157 4158 age <= 55.0 entropy = 1.0 samples = 2 value = [1, 1] 4156->4158 4159 entropy = 0.0 samples = 1 value = [1, 0] 4158->4159 4160 entropy = 0.0 samples = 1 value = [0, 1] 4158->4160 4164 race_White <= 0.5 entropy = 0.439 samples = 11 value = [10, 1] 4163->4164 4167 entropy = 0.0 samples = 2 value = [0, 2] 4163->4167 4165 entropy = 0.0 samples = 1 value = [0, 1] 4164->4165 4166 entropy = 0.0 samples = 10 value = [10, 0] 4164->4166 4169 age <= 52.5 entropy = 0.883 samples = 63 value = [19, 44] 4168->4169 4224 entropy = 0.0 samples = 3 value = [3, 0] 4168->4224 4170 hours-per-week <= 46.5 entropy = 0.811 samples = 56 value = [14, 42] 4169->4170 4219 age <= 59.0 entropy = 0.863 samples = 7 value = [5, 2] 4169->4219 4171 sex_Male <= 0.5 entropy = 0.958 samples = 29 value = [11, 18] 4170->4171 4202 sex_Male <= 0.5 entropy = 0.503 samples = 27 value = [3, 24] 4170->4202 4172 age <= 41.0 entropy = 0.811 samples = 8 value = [6, 2] 4171->4172 4181 age <= 32.0 entropy = 0.792 samples = 21 value = [5, 16] 4171->4181 4173 entropy = 0.0 samples = 3 value = [3, 0] 4172->4173 4174 workclass_Private <= 0.5 entropy = 0.971 samples = 5 value = [3, 2] 4172->4174 4175 age <= 43.0 entropy = 0.811 samples = 4 value = [3, 1] 4174->4175 4180 entropy = 0.0 samples = 1 value = [0, 1] 4174->4180 4176 workclass_Public <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 4175->4176 4179 entropy = 0.0 samples = 2 value = [2, 0] 4175->4179 4177 entropy = 0.0 samples = 1 value = [1, 0] 4176->4177 4178 entropy = 0.0 samples = 1 value = [0, 1] 4176->4178 4182 entropy = 0.0 samples = 1 value = [1, 0] 4181->4182 4183 workclass_Public <= 0.5 entropy = 0.722 samples = 20 value = [4, 16] 4181->4183 4184 workclass_Private <= 0.5 entropy = 0.523 samples = 17 value = [2, 15] 4183->4184 4197 hours-per-week <= 42.5 entropy = 0.918 samples = 3 value = [2, 1] 4183->4197 4185 entropy = 0.0 samples = 5 value = [0, 5] 4184->4185 4186 age <= 35.0 entropy = 0.65 samples = 12 value = [2, 10] 4184->4186 4187 entropy = 0.0 samples = 2 value = [0, 2] 4186->4187 4188 age <= 49.0 entropy = 0.722 samples = 10 value = [2, 8] 4186->4188 4189 age <= 46.0 entropy = 0.811 samples = 8 value = [2, 6] 4188->4189 4196 entropy = 0.0 samples = 2 value = [0, 2] 4188->4196 4190 education <= 15.5 entropy = 0.592 samples = 7 value = [1, 6] 4189->4190 4195 entropy = 0.0 samples = 1 value = [1, 0] 4189->4195 4191 entropy = 0.0 samples = 5 value = [0, 5] 4190->4191 4192 hours-per-week <= 38.0 entropy = 1.0 samples = 2 value = [1, 1] 4190->4192 4193 entropy = 0.0 samples = 1 value = [0, 1] 4192->4193 4194 entropy = 0.0 samples = 1 value = [1, 0] 4192->4194 4198 age <= 47.5 entropy = 1.0 samples = 2 value = [1, 1] 4197->4198 4201 entropy = 0.0 samples = 1 value = [1, 0] 4197->4201 4199 entropy = 0.0 samples = 1 value = [0, 1] 4198->4199 4200 entropy = 0.0 samples = 1 value = [1, 0] 4198->4200 4203 entropy = 0.0 samples = 13 value = [0, 13] 4202->4203 4204 education <= 15.5 entropy = 0.75 samples = 14 value = [3, 11] 4202->4204 4205 age <= 45.0 entropy = 0.881 samples = 10 value = [3, 7] 4204->4205 4218 entropy = 0.0 samples = 4 value = [0, 4] 4204->4218 4206 hours-per-week <= 57.5 entropy = 0.954 samples = 8 value = [3, 5] 4205->4206 4217 entropy = 0.0 samples = 2 value = [0, 2] 4205->4217 4207 age <= 36.0 entropy = 0.863 samples = 7 value = [2, 5] 4206->4207 4216 entropy = 0.0 samples = 1 value = [1, 0] 4206->4216 4208 entropy = 0.0 samples = 2 value = [0, 2] 4207->4208 4209 hours-per-week <= 52.5 entropy = 0.971 samples = 5 value = [2, 3] 4207->4209 4210 age <= 40.5 entropy = 0.918 samples = 3 value = [2, 1] 4209->4210 4215 entropy = 0.0 samples = 2 value = [0, 2] 4209->4215 4211 entropy = 0.0 samples = 1 value = [1, 0] 4210->4211 4212 workclass_Public <= 0.5 entropy = 1.0 samples = 2 value = [1, 1] 4210->4212 4213 entropy = 0.0 samples = 1 value = [0, 1] 4212->4213 4214 entropy = 0.0 samples = 1 value = [1, 0] 4212->4214 4220 entropy = 0.0 samples = 4 value = [4, 0] 4219->4220 4221 age <= 64.5 entropy = 0.918 samples = 3 value = [1, 2] 4219->4221 4222 entropy = 0.0 samples = 2 value = [0, 2] 4221->4222 4223 entropy = 0.0 samples = 1 value = [1, 0] 4221->4223
In [36]:
system(dot -Tpng tree.dot -o dtree.png)
Out[36]:
['dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.385462 to fit']
In [37]:
from IPython.display import Image
Image(filename='dtree.png', width=800)
Out[37]: